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POSTMINING PALIMPSEST

ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES PROGRAMME: EMERGENT TECHNOLOGIES AND DESIGN YEAR: 2024-2025 COURSE TITLE: MSc. Dissertation DISSERTATION TITLE: POST-MINING PALIMPSEST STUDENT NAMES: Mauli Patel (Msc) Sung-Soo Park (Msc) Ajinkya Randive (MArch) Luis Castro Aguilar (MArch)

DECLARATION: “I certify that this piece of work is entirely my/our and that my quotation or paraphrase from the published or unpublished work of other is duly acknowledged.” SIGNATURE OF THE STUDENT:

(Sung-Soo Park)

(Mauli Patel)

DATE: 19 September 2025

Architectural Association School of Architecture Master of Science in Emergent Technologies and Design 2024-2025

Course Director Dr. Milad Showkatbakhsh

Founding Director Dr. Michael Weinstock

Studio Tutors Abhinav Ranjan Chaudhary Danae Polyviou Paris Nikitidis Felipe Oeyen

ACKNOWLEDGMENT We express our deepest gratitude to our course founder Dr. Michael Weinstock, as well as our director, Dr. Milad Showkatbakhsh, along with our tutors: Felipe Oeyen, Paris Nikitidis, Abhinav Chaudhary, Dr. Alvaro Velasco Perez, and Danae Polyviou for their invaluable support and guidance throughout this year and our thesis journey. We are profoundly grateful to our family and friends for their continuous encouragement and support.

ABSTRACT This dissertation addresses the critical environmental degradation resulting from coal mining activities in Jharia, India. This region represents the devastating legacy of extractive industries, characterised by land subsidence, extensive contamination of soils and aquifers, and the almost total loss of local ecosystems. The team focuses on the problem of abandoned coal mining terrains. From this the team investigates the process of coal extraction. Each stage has been analysed sequentially, from the initial deforestation and removal of the overburden, through the actual extraction, to the final abandonment of the excavated sites, and from there explaining how each stage inflicts distinct and often irreversible damage. These operations have rendered large tracts of land unstable, uninhabitable and biologically sterile, creating complex environmental hazards. In addition, the study examines the intricate socioenvironmental dynamics, exploring how these degraded landscapes are intertwined with surrounding human settlements and activities. These dynamics generate cycles of vulnerability and economic scarcity among inhabitants adjacent to abandoned mining areas, and in most cases are enduring. The magnitude and persistence of damage in Jharia underscores the urgent need for effective and scalable remediation strategies. In response to this reality, this research proposes a cyclical remediation system. The strategic development of a bioreceptive structural material is proposed as the core of the approach. The objective is to specifically design a system that fulfills the functions of stabilising dangerously unstable soils, generating a soil regeneration process and performing different functions as it adapts to the characteristics of the terrain, the most important of which is sustainable architectural aggregation. This process facilitates phytoremediation and soil rehabilitation, actively promotes revegetation and restoration of agricultural potential, and ultimately establishes a stabilised foundational structure. This structure is designed to enable sustainable architectural and housing development, thereby catalysing holistic regional regeneration. The ultimate goal is to establish a transferable systemic framework, demonstrably applicable in Jharia and easily adaptable to degraded landscapes following mining exploitation worldwide. It aims to demonstrate that environmentally degraded areas can be transformed into revitalised and productive ecosystems.

1. Vishal Kumar Singh, The Burning City: A Photographic Documentary on Jharia (India, n.d.).

Fig. 0.0. View of Jharia’s Mine Scapes. Photograph from Vishal Kumar Singh, The Burning City: A Photographic Documentary on Jharia (India).

Fig. 0.1. Image of a Design Intervention

CONTENTS

01............... INTRODUCTION 02............... THE DOMAIN 2.1

Coal Mines - A Global phenomenon

2.2

Evolution of Coal Mining in Jharia

2.3

2.4

2.5

2.6

Global Footprint

Introduction to the Mining Belt Historical Evolution of Extractivism

Environmental Degradation and Pollutants Air pollutants Geotechnical Instability Soil & Water pollutants & Vegetation Loss

Coal Mining

Mining cycle in Jharia Fractured Landscape (abandoned Mines) Dynamics and Transport of Coal Land Cover Map

Socio-Economic Consequences Living in the Mine Lands Health Impacts Economic Dependencies Displacement and Dysfunction

Building Cultures

Vernacular building methods Building with mine materials Displacement and Dysfunction Adapted Material Systems from Mining Wastes

2.7

Global Lessons

2.8

Discussion

Case Studies and establishing relationships

Research Question and Proposal Bibliography

03............... METHODS 3.1

The System : Environment to Data Terrain and Data Patching Agent-Based Modelling Generator Multi-Objective Optimisation Selector Clustering

RESEARCH DEVELOPMENT 4.1 The System : Decoding Risk Modeling Priority Intervention Zones

4.2 Ecological Logic : Guild Strategy Corridor guild strategy

4.3 The Settlement : Pre-Study

The program Patch selection and cluster criteria

4.4 The Material : Performance

From mining waste to printable matter Biochar and clay as bio receptive composites Test printability and bioreceptivity Robotic 3D printing experiments

4.5 The Structure : Form-finding Material and Planting Interplay

DESIGN DEVELOPMENT 5.1 The System : Searching

Agent-Based Network Exploration Multi-Objective Optimisation Corridor Risk Analysis and Intervention Strategy Ecological Planting Strategy: The Guild Logic Ecological Planting Strategy: Ecologic

5.2 The Settlement : An Algorithmic Masterplan The Clustering Logic for a Productive Settlement

5.3 The Structure : An Integrated Eco-Structural System Ground–structure interaction Multi-Objective Optimisation

5.4 The Material : Fabrication

Material Experiments and Prototyping Fabrication Strategy: Robotic 3D Printing on Terrain

DESIGN PROPOSAL 6.1 The Masterplan : Integrated Stabilisation Corridor Final route and priority segments Hydrology and treatment coupling

6.2 The Structure : Bio-Receptive System

3.2

Ecological Intelligence

3.3

The Material : From Waste to Resource Robotic printing strategies on sloped terrain

Phased Growth: Architectural Expansion Over Time A Speculative Vision for a Post-Mining Palimpsest

3.4

The Structure : Generative Design

CONCLUSION

Guilds and Suitability Patch to Plant to Porosity

Plant interface Cluster distribution

6.3 The Palimpsest

Discussion of Research and Contributions Limitations and Future Work

BIBLIOGRAPHY APPENDIX

INTRODUCTION The Jharia Coalfield has served as the heart of the Indian coal industry for over a century. Though it still contributes significantly to the national economy, its indiscriminate mining activities have led to extensive environmental degradation. It is a landscape characterised by abandoned open-pit mines where extraction has ceased, vast piles of discarded overburden dumps, adjoining ongoing active mining operations. This devastated landscape is however, becoming a familiar landscape in multiple regions around the world marked by the effects of extractivism. This overlapping of past and present mining activities inherently creates a compounded set of repercussion. The mining activities have caused ground subsidence, affecting residential areas, and emitting toxic gases, posing a severe threat to the health of the local population. The soil and groundwater are contaminated with acid mine drainage and heavy metals, jeopardizing both ecosystems and drinking water sources. For Jharia, these problems are exacerbated by its monsoon climate and complex geological structure that accelerate the spread of pollution, exacerbating risks such as water scarcity and waterborne diseases. Jharia Coalfield is at the crossroads of contemporary post-extractivist debates. These poses high pressure on the local population. Government-mandated relocation policies, which often lack realism and sustainability. These displaced individuals eventually return to the mining areas in search of employment, trapped in a vicious cycle of poverty and pollution. Therefore, there is a cycle between the coal mining labor, subsidence soil problems, lack of job opportunities and working conditions. Mining in Jharia has not only displaced people physically but has also fragmented social structures, livelihoods, and cultural memory. The aim is to investigate how architecture can become a medium for healing both spatially and socially, creating safe environments where architecture can respond to not only spatial issues but health and environmental issues.

Fig. 1.0. Diagram of a Coal Mine Landscape in Jharia

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Fig. 1.1. People Working in the Coal Field. Photograph from Vishal Kumar Singh, The Burning City: A Photographic Documentary on Jharia (India).

The Domain

THE DOMAIN This chapter aims to establish a deep understanding of the area to be addressed, the mining processes in Jharia, and case studies that will help us tackle the issue. First, we examine the complete mining cycle, from prospecting to abandonment, identifying its spatial, ecological, and social footprints. It also examines the economic and social dynamics associated with extractivism, particularly those linked to the coal economy, job losses following mine closures, and the processes of displacement and territorial disarticulation that often follow the depletion of these systems. Indicators of environmental degradation, social exclusion, and economic stagnation are analysed in order to recognise the barriers in this specific context. Finally, case studies are presented that have successfully converted post-extractive landscapes into regenerative productive, cultural, or ecological systems. These precedents allow for the identification of relevant strategies, applied technologies, and governance models that serve as references for the proposed project.

1. Vishal Kumar Singh, The Burning City: A Photographic Documentary on Jharia (India, n.d.).

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Inactive Mines Active Mines

Fig. 1.2. Active and Inactive Coal Mines around the glove (adapted from Global Coal Mine Tracker, Global Energy Monitor, 2025).

The Domain

2.1 Coal Mines - A Global Phenomenon

Mining landscapes shaped by extractivist operations whether for copper, gold, chromite, coal, or stone span vast tracts of land and radically transform the earth’s surface. These operations often involve expansive open pits and large dumps of waste material, forming disrupted terrains of considerable scale. Globally, approximately 33,000 mines occupy land reflecting both their physical and environmental magnitude.1 Coal mining, in particular, has a century-long legacy, driven largely by industrial and energy needs. However, with increased awareness of global warming and the high carbon emissions from coal combustion, stringent regulations have led to a reduction in coal mining. There are about 4732 global ongoing coal mine sites, nearly 2244 have been shut down or abandoned.1 The closure of mine sites has also triggered socio-economic consequences. These operations often employ thousands, many of whom are migrant or informal workers. The decline of mining in certain regions has resulted in unemployment, forced relocation, and a loss of community structures, especially where alternative livelihoods are limited. This presents a dual challenge: environmental degradation and social dislocation. Amid global environmental crises and socio-economic transition, these landscapes call for new ways of imagining their future. What should happen to these vast, scarred terrains? Can they be remediated to restore ecological balance while also addressing the needs of affected communities. These lands pose challenges in socio-ecological Justice, governance and accountability, economic alternatives, climate resilience and landscape futures.

1. Global Energy Monitor, Global Coal Mine Tracker, 2025 release, accessed September 17, 2025, https://globalenergymonitor.org/projects/global-coal-mine-tracker/

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2.2 Evolution of Coal Mining in Jharia

2.2.1 Introduction to the Mining Belt

Coal mining, a global phenomenon, has concurrently served as an engine for industrial development and a source of irrevocable damage in specific regions. The Jharia Coalfield (JCF) in Jharkhand, India, stands as a prominent case study. Spanning a vast area of approximately 450 km², it is India’s primary source of coking coal¹. However, its economic significance is overshadowed by a severe environmental catastrophe, systematically accumulated over a century, which establishes Jharia as a critical subject for analysing the destructive outcomes of extractivism. INDIA

JHARKHAND

Fig. 1.3. Map of BCCL Mining Areas, Urban Settlements, and Site Location within the Jharia–Dhanbad Region(author)

1. Anupal Jyoti Dutta et al., “Investigations of Geothermal Energy Production in Coal Fires Affected Jharia Basin, India,” Proceedings of the 49th Workshop on Geothermal Reservoir Engineering (Stanford University, 2024): 1, lines 12–13. 2. B. P. Jha, “Unscientific Mining and Its Social Impact: A Case Study of Jharia Coalfield, India,” Rupkatha Journal on Interdisciplinary Studies in Humanities 3, no. 2 (2011): 253–255. 3. G.S. Saini, “Environmental impact of opencast and underground mining in Jharia coalfield, India,” Environmental Geology 20 (1992): 185–190.

The Domain

2.2.2 Historical Evolution of Extractivism

The history of Jharia clearly illustrates how extractivism has evolved through different modalities over time, degrading the region’s physical and social environment. Phase 1: Underground Mining and the Onset of Disaster in the Colonial Era (1890s–1940s) Following the establishment of the railway in 1894, Jharia’s mining history was dominated by the ‘pillar and gallery’ method of underground extraction. This method, aimed at maximising short-term output, fundamentally compromised the structural integrity of the mines through the excessive extraction of coal pillars, in disregard of safety regulations. Such indiscriminate and unscientific mining practices led to large-scale collapses in the 1930s, and the resultant underground fires were the prelude to a disaster that persists to this day². Phase 2: The Shift to Opencast Mining and the Intensification of Environmental Degradation (1970s–Present) After the nationalisation of the coal industry in the 1970s, opencast mining was introduced on a massive scale to meet India’s energy demands, altering the nature of extractivism. This approach entailed the complete removal of the surface, causing far more extensive and direct environmental destruction than before³. From this period, the underground fires became increasingly uncontrollable; it is reported that approximately 70 fires are currently active within the Jharia Coalfield, the highest number of any coalfield in India⁴. The Legacy of Evolving Environmental Crisis

Extractivism:

Compound

Thus, the evolution of mining practices in Jharia over a century has left a legacy of an unmanageable environmental disaster. Specifically, a feedback loop has been established wherein underground fires create voids by burning coal, which leads to land subsidence; the resulting surface fissures then supply oxygen that exacerbates the fires⁵. This geological instability has reached a critical level, threatening settlements and essential transport networks⁶. The vast waste dumps, air pollution, and contamination of soil and water generated in this process have permanently altered Jharia’s landscape, and are the direct cause of the Environmental Degradation and Pollutants to be discussed in the subsequent chapter.

4. Vamshi Karanam et al., “Multi-sensor Remote Sensing Analysis of Coal Fire Induced Land Subsidence in Jharia Coalfields, Jharkhand, India,” International Journal of Applied Earth Observation and Geoinformation 102 (2021): 102439. 5. Karanam et al., “Multi-sensor Remote Sensing Analysis,” 2:17–18. 6. Karanam et al., “Multi-sensor Remote Sensing Analysis,” 2:7–8.

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Fig. 1.4. Air Pollutant Concentration Map. (adapted from V. Saini, “Environmental impact studies in coalfields in India: A case study of the Jharia coalfield,” Renewable and Sustainable Energy Reviews 2016).

The Domain

2.3 Environmental Degradation and

Pollutants

This section investigates the environmental consequences of extractive activities, focusing on the degradation processes and pollutants associated with coal mining. Coal mining, especially in its open-pit form, leaves behind severely altered landscapes characterised by soil erosion, loss of vegetation, altered hydrology, and the accumulation of toxic substances. We analyse the biogeochemical impact of these pollutants, exploring how they affect the earth’s capacity for regeneration and how they disrupt the development of healthy ecosystems. It also examines the spatial distribution of pollution, the ecotoxicological risks to surrounding communities, and the legacy of environmental injustice often linked to extractive frontiers.

2.3.1 Air pollutants

The most imminent issue of air pollution caused by underground fires from burning coal seams which have been burning for decades. In Jharia, AQI(air quality index) often exceeds 150–200, which falls in the “Unhealthy” range and this is particularly driven by high PM₂.₅ and PM₁₀ levels from coal fires and dust pollution.1 PM2.5 and PM10 are 2.5 micrometers and 10 micrometers or less, respectively where the former is small enough to penetrate into the lungs and bloodstream, and the latter capable of causing irritation to the eyes and throat, both leading to health issues. From 2010 to 2013, asthma cases jumped by 92%, and surveys found 42% of residents suffering from chronic bronchitis or COPD.1 In addition, mining activities such as overburden dumping release fine particles that travel with the wind, with dust and trace metals. Future construction risks highlight two architectural priorities: protecting against air pollution near affected sites and stabilizing overburden dumps to limit pollutant spread, while also addressing how communities can safely co-exist with nearby pollution sources.

1. IEASRJ; Times of India 2. V. Saini, “Environmental impact studies in coalfields in India: A case study of the Jharia coalfield,” Renewable and Sustainable Energy Reviews 2016, accessed [Date you viewed the article], https://www. sciencedirect.com/science/article/abs/pii/S1364032115010424 .

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Fig. 1.5. Ground Subsidence Map. (adapted from R. S. Chatterjee et al., “Detecting, mapping and monitoring of land subsidence in Jharia Coalfield, Jharkhand, India by spaceborne differential interferometric SAR, GPS and precision levelling techniques,” 2015). 1. Moidu Jameela Riyas, Tajdarul Hassan Syed, Hrishikesh Kumar, and Claudia Kuenzer, “Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data,” Remote Sensing 13, no. 8 (2021): article 1521, https://doi.org/10.3390/rs13081521. mdpi.com 2. Global Journals Inc., “Impact of Coal Mining on Environment in India,” Global Journal of Human Social Science XIII, no. VIII (2013), detailing pyrite oxidation and heavy‑metal contamination in Jharia mining zones. 3. IEASRJ; Chapman University, “Underground Burning of Jharia Coal Mine (India) and Associated...”.

The Domain

2.3.2 Geotechnical Instability

The pervasive ground subsidence and persistent underground fires in Jharia’s abandoned coal mines area caused due to two interlinked processes triggered by desiccation which is the severe drying of subsurface materials. Firstly, prolonged desiccation eliminates natural moisture that binds geological strata. Clay-rich sediments and coal rocks, deprived of water, shrink and fracture causing fissures and voids, destabilising mine galleries and support pillars. Over time, the weakened ground collapses, causing surface subsidence and sudden sinkholes or gradual land depression.1 Simultaneously, desiccation makes the exposed coal seams highly combustible and pyrophoric which means its increased porosity allows rapid oxidation, generating heat that can ignite it spontaneously at temperatures as low as 80°C. Friction from collapsing structures, residual electrical faults, or even smallscale landslides provide ignition sources. Once ignited, fissures formed by subsidence act as ventilation ducts, channeling fresh oxygen to fuel the flames. This airflow transforms localised fires into self-sustaining infernos that spread through interconnected seams. 2 In Jharia, over 77 active fire zones feed this cycle, with subsidence displacing communities and fires releasing toxic gases especially in unsealed abandoned mines where desiccation remains unchecked. 3 4 However, under the Jharia Rehabilitation Plan, measures have been taken to suppress the fires using techniques such as inert gas injection and sand filling which have reduced fires from 17.32 km² in 2009 across 77 active fire sites just 1.8 km² in 2021, covering 27 fire sites.5

4. Global Bihari, “Considerable Reduction in Surface Fire Area in Jharia Claims Coal Ministry,” accessed June 2025, https://globalbihari.com/considerable-reduction-in-surface-fire-area-in-jharia-claims-coalministry/. 5. Ministry of Coal, Government of India. “Jharia Master Plan: Coal Ministry Efforts Bring Down Surface Fire Identified from 77 to 27 Sites.” Press Information Bureau press release, September 25, 2023. Accessed June 25, 2025. 6. R. S. Chatterjee et al., “Detecting, mapping and monitoring of land subsidence in Jharia Coalfield, Jharkhand, India by spaceborne differential interferometric SAR, GPS and precision levelling techniques,” Journal/Conference (2015), accessed September 17, 2025, via ResearchGate: https://www.researchgate.net/figure/Combined-land-subsidence-areas-in-Jharia-Coalfield-as-obtained-from-C-and-L-bandDInSAR_fig5_282245541 21 .

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Fig. 1.6. Vegetation density map. (author)

The Domain

2.3.3 Soil & Water pollutants & Vegetation Loss

The excavated landscapes of pits cause water collection and the rainfall on the dumps carry pollutants to its immediate low lying lands and water streams that ultimately connect to the damodar river.1 Studies by the Indian School of Mines show the topsoil has low moisture content, poor pH balance, and depleted essential nutrients like nitrogen, potassium, and phosphorus, making it unfit for farming or plant growth. One significant environmental concern associated with this is Acid Mine Drainage (AMD) which occurs when sulfide minerals are exposed to air and water resulting in the formation of sulfuric acid. The soil has traces of heavy metals like arsenic, selenium, mercury, lead, sulphur, and fluorine. The Damodar river water has transformed into acidic and sludge-choked channels, with recorded pH levels as low as 2.5 making the water lethal to aquatic life and unusable for human activities.2 Coal fines create thick, black sedimentation that suffocates riverbeds, while heavy metals bioaccumulate in fish and agricultural produce. Communities relying on the Damodar River face contaminated drinking water with arsenic concentrations documented at fifty times the World Health Organisation’s safety limits, alongside poisoned irrigation water that reduces crop yields and compromised food security. 2 A structured remediation cycle is essential to meet regulatory requirements, sustain ongoing mining operations, and repurpose abandoned sites. While high-intensity methods such as soil washing, chemical stabilization, and ex-situ containment might be resource intensive , bioremediation and phytoremediation are preferred for their sustainability and ecological compatibility. This remediation-led approach indicates a cyclical model of recovery and urban transformation, and the landscape acts as a foundation enabling regenerative land use in post-industrial terrains.

1. V. A. Selvi et al., “Impact of coal industrial effluent on quality of Damodar river water,” Indian Journal of Environmental Protection 32, no. 1 (January 2012): 58–65 2. Abhay Kumar Singh et al., “Major Ion Chemistry, Weathering Processes and Water Quality Assessment in Upper Catchment of Damodar River Basin, India,” Environmental Geology 54, no. 5 (2008): 745-58..

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2.4 Coal Mining It is necessary to understand how the mineral is formed and why its extraction is so important to the industry. This analysis will help us understand why it forms, how this determines its location, and how this in turn conditions the methods of extraction. Coal is a sedimentary rock that is formed when plant material decomposes over millions of years. It is composed mainly of carbon (65-95%), along with hydrogen, oxygen, nitrogen, sulphur and various minerals. Coal comes from the accumulation of dead plant material in swampy areas and wetlands approximately 320 million years ago 1. When this plant matter died and found itself in areas with water, it did not allow for normal decomposition of the matter due to the lack of oxygen. Bacteria began to decompose the material in a slow and controlled process, and it is during this process that the carbon content is retained. Over the years, the environment around it became rich in carbon. The decomposition of these plants continues, increasing the depth of burial and generating new layers on top of the previous dead matter, which creates pressure and increases the temperature of the lower layers. Thus, each layer is transformed according to its depth, resulting in different layers, each with a higher amount of carbon and energy density 1. The four main types of coal, according to their carbon content, are peat, lignite, bituminous coal and anthracite. Peat is the earliest stage of coal and has a high moisture content. Lignite (25-30% carbon) is matt black in colour and marks the skin. Bituminous coal (70-90% carbon) is the most commonly used for energy production, and anthracite (90-97% carbon) 2 is the highest quality coal. This is why coal is found in layers within earthquakes, which are known as seams.

There are two main methods for extracting coal: open-pit mining and underground mining. The choice of extraction method depends primarily on the depth and location of the coal seams. Open-pit mining is used when coal seams are no deeper than 60-70 metres, allowing large amounts of soil and rock to be removed to directly access the coal. This method is more economical, safer and more efficient in terms of production, as it allows the use of heavy machinery and continuous operation. The simple fact that it allows easy access to heavy machinery significantly reduces costs. In other words, processes such as excavation, extraction and transport are more fluidly controlled. On the other hand, underground mining is used when the seams are deeper; in this case, tunnels must be excavated to access the resource, which involves higher costs, occupational hazards, continuous ventilation and a different environmental impact, because although it alters the surface less, it can cause subsidence or sinking. 3 This type of extraction is much more expensive because it requires extensive tunnel networks, which must be accompanied by structures to ensure safety. Although this method is less invasive on the surface, it poses enormous safety challenges that translate into higher investment. In the case of Jharia, India, where this research is concentrated, open-pit mining is predominant because many of the coal seams are shallow, allowing for more direct access. In addition, low operating costs and high demand for coal have encouraged this type of large-scale extraction. However, this extraction method has serious environmental and social implications, such as air pollution from spontaneous coal fires, displacement of communities, and total alteration of the landscape.

1. thedailyECO, “How Is Coal Made?,” thedailyECO, November 19, 2024, https://www.thedailyeco.com/how-is-coal-made-877.html

The Domain

Fig. 1.8. Diagram explaining the process of coal formation

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2.4.1 Mine Cycle in Jharia

In this historical context, and to propose a remediation strategy for open-pit mines, it is important to understand the mining cycles in Jharia and their implications for the surrounding environment. It is important to understand the reasons for these processes. The mining cycle begins with exploration, during which samples are taken from areas with potential coal deposits. Geophysical explorations are conducted to measure the seismic, electrical, magnetic, radiometric, and gravitational properties of materials to detect the structural characteristics that define coal deposits.1

into tributaries to the Damodar River, and air pollution due to explosions, excavation, loading and unloading of coal. This constant movement of material raises ambient PM₁₀ levels to between 3 and 4 times India’s safe limits , and sulfur compounds in dust contribute to atmospheric acidification. This intense acidity, in combination with the region’s significant annual precipitation (1,200 mm), accelerates the acidification of soils, freshwater bodies, and vegetation across extensive areas downwind of the coalfields, extending environmental damage far beyond active mining zones. Monitoring data reveals Jharia’s annual rainfall registers a profoundly acidic pH of 4.2–4.8 , starkly lower than India’s average range of 5.5–6.0.3

After the exploration process and reliable mineral existence data, extraction begins. It is important to note that the mines in the Jharia area are open-pit mining, due to the large quantities of ore to be extracted found at a distance of no more than 60 m from the surface. When it is deeper than this, underground mining is used, and due to the exploitation costs as open-pit mining is more profitable. To begin this stage, vast areas are deforested, often 5 to 10 km² per mine. Once deforestation is complete, the surface layer is removed. The first step is to fracture this layer through controlled blasting and then excavate it with draglines.2 Once the extraction is complete, the beneficiation process begins, which involves coal washing.1 This process is carried out in nearby plants. In these plants, the coal is crushed into various particles and then separated from impurities. Once separated, it is washed to remove the last remaining dust particles. Finally, the mineral is transported, where the processed coal is poured into train cars and transported to steel plants and hydroelectric power plants. The coal mining process results in the following issues of environmental pollution: irreversible biodiversity loss due to mineral excavation and exploitation, overburden from excavation that remains as remnants in the landscape, wastewater from the beneficiation process laden with heavy metals such as arsenic, lead and pyrite that is discharged

1. How coal mining works. https://bkvenergy.com/learning-center/how-coal-mining-works/. 2. The coal mining life cycle. https://miningforschools.co.za/lets-explore/coal/the-coal-mining-life-cycle. 3. V. A. Selvi et al., “Impact of coal industrial effluent on quality of Damodar river water,” Indian Journal of Environmental Protection 32, no. 1 (January 2012): 58–65

The Domain

Fig. 1.9. Coal mining cycle

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Fig. 1.10. Illegal Mining in Jharia(Photograph from Just Energy Transition: Economic Implications for Jharkhand, Climate Policy Initiative, 2023).

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2.4.2 Fractured Landscape

After explaining how coal is formed and the processes involved in its extraction, the aim has been to provide a foundation for the reader to better understand the implications of this type of human activity. First and foremost is the physical degradation of the affected area, mainly as a result of open-pit mining. The holes generated through this method of extraction are approximately 500 metres in radius and between 70 and 80 metres deep. These areas were once home to functioning ecosystems, with flora and fauna that can no longer be recovered. During coal mining operations, plant and animal species are lost, along with agricultural activities and other forms of local land use. The mining pits ultimately become physical barriers within the existing ecosystem.1 Moreover, the extraction process generates large quantities of debris which are essentially the overburden removed to access the coal seams. It is estimated that for each mining pit, between 8 and 11 cubic metres of infertile soil are displaced. This material is often left near the mining sites, becoming a permanent part of the altered landscape. From a geotechnical perspective, the large-scale movement of earth and the accumulation of debris create unstable zones around the mining area. This leads to risks of landslides, ground subsidence, and rockfall, which make the area difficult and dangerous to access or repurpose. Another key concern is soil degradation. As mentioned earlier, above the desired coal seam lie layers of material with lower carbon content, many of which contribute to soil acidity once exposed. In other words, the debris is not simply soil, it also contains carbon-rich material which, through excavation and transport, contaminates the surrounding land. This brings us to the issue of air pollution. When the land is disturbed, dust containing a high proportion of coal particles is released into the air, contributing significantly to local air pollution. Additionally, in many cases the coal is washed onsite using machinery brought in for this purpose. This cleaning process consumes large quantities of water, and the resulting wastewater is often discharged into nearby bodies of water, in this case, the Damodar River. Amidst this degraded landscape, local inhabitants attempt to find economic opportunities linked to the coal industry. Over time, this has led to the emergence of informal settlements in and around mining zones. However, the living conditions in these areas are far from adequate.

1. Just Energy Transition: Economic Implications for Jharkhand, by Md Tariq Habib, Saarthak Khurana, and Vivek Sen (Climate Policy Initiative, December 28, 2023), accessed September 17, 2025, https:// www.climatepolicyinitiative.org/just-energy-transition-economic-implications-for-jharkhand/

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Fig. 1.10. Map showing the distribution of coal deposits around Jharia.

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2.4.3 Dynamics and Transport of Coal

The map illustrates the spatial relationship between coal mining, industrial infrastructure and human settlements in the Jharia coalfield. It shows the presence of two major coal washing plants and how these plants are connected by railway lines that facilitate the constant movement of coal in and out of the region. These facilities are essential to the industrial system, as they clean and sort the coal before transport, but in doing so they generate large amounts of waste. The waste from this process, especially fly ash, is released into the atmosphere and dispersed over long distances, settling not only in nearby mines, but also in surrounding villages and agricultural fields. This industrial footprint extends beyond the pits themselves. Settlements are often located in close proximity to both the washery plants, mining pits and the overburden waste zones, meaning that daily life unfolds in direct contact with the consequences of coal processing. The presence of fine coal dust and fly ash contributes to air pollution, while contaminated runoff frequently enters local streams and eventually the Damodar River, one of the principal waterways in Jharkhand. This contamination alters the ecological balance of the region, undermining water quality and threatening agricultural productivity, while simultaneously degrading urban environments through the constant deposition of airborne particulates. What emerges from this mapping is the image of a landscape in which coal is not confined to mines, but circulates in the form of dust, ash and waste, infiltrating homes, fields and rivers. The impact is both environmental and social, as fine particles of fly ash settle on agricultural land, accumulate in urban spaces and infiltrate domestic life, leaving little distinction between places of production and places of habitation. However, this situation presents a crucial opportunity. Instead of perceiving fly ash solely as an unwanted pollutant, we are beginning to approach it as a material option. Its presence throughout the territory challenges us to reconsider its role, not only as a waste product of extraction, but as a substance with latent potential. By reframing it as a potential resource, this byproduct of coal production can move from the margins of waste management to the centre of experimental design. In this sense, the environmental crisis itself offers a starting point for material innovation, with fly ash as the basis for the composition of our experimental material.

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Fig. 1.12. Diagram showing Chosen Abandoned Mine and Activties in the Vicinity. Photographs included from Newsclick (2022) and Business Insider (2018).

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1. Photographs in Figure 5.6 from Newsclick, “Jharia’s Coal Mining-Affected Families Continue to Live Atop Tinderbox,” by Ayaskant Das, January 13, 2022, https://www.newsclick.in/jharia-coal-miningaffected-families-live-atoptinderbox 2. Business Insider, “Dhanbad, India: Coal Capital of the World,” by Sebastian Sardi, October 2018, https://www.businessinsider.com/dhanbad-india-coal-capital-of-the-world-2018-10

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2.4.4 Land Cover Mapping

One such abandoned mine land in proximity to Jharia is identified which has undergone excavation such that the area which was a pit in 2016 was backfilled to excavate at the location of the dump. Current abandoned state has lead to water logging and satellite imagery shows the transformation of the site and its impacts on the immediate land cover.

2016

Mapping the ground cover of the current site condition reveals the immediate landscapes around the pit which includes scrub lands and dense ground covers around the water stream. The water flow from the dumps carry coal residues and other metals directly into the water stream that connects directly to the Damodar river. The built fabric is interspersed with mine lands and dust and air pollutants become imminent issues.

2019

OB dump Mine wasteland Coal residues

2021

Stone Scrubland Dense trees Small trees Vegetation around water stream Water Built

2024 Fig. 1.13. Land Transition over given time period. Image modified from Sentinel-2 Satelline imagery

Roads

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Fig. 1.14 . Land cover map drawn from data extrapolated from Satellite imagery( author)

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Fig. 1.15. People Living adjacent to the Coal Field. Photograph from Vishal Kumar Singh, The Burning City: A Photographic Documentary on Jharia (India).

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2.5 Socio-economic Consequences The Jharia coalfields represent a spatial paradox: a site rich in national energy potential yet impoverished in the daily life of its people. Decades of unregulated extraction have produced enormous wealth for the state and industry, but at the cost of local livelihoods, land, and dignity.1 What emerges is a fractured socio-economic fabric where opportunity exists only in the service of coal, an extractive logic that translates directly into spatial neglect. Settlements near the mines are structurally informal and infrastructurally barren. Educational access is limited, mobility is constrained, and financial resilience is nearly non-existent. The people remain suspended in a mono-industrial economy without alternative pathways. Their informal economies, scavenging, day labour, and subsistence trade operate in spaces never designed to support human activity. 2 These are environments that cannot grow because the system itself denies growth. This has critical implications for architecture: the space of Jharia is not just under-designed, it is under-imagined. Addressing socio-economic paralysis demands new spatial logic systems that introduce redundancy, flexibility, and opportunity into otherwise dead-end conditions. The architectural response must go beyond housing or infrastructure; it must actively re-script spatial economies, enabling communities to generate, share, and sustain value outside the shadow of extraction.

2.5.1 Living in the Mind Lands- Health Impacts

In Jharia, health is not just a function of care access; it is deeply entangled with environmental exposure. Air carries particulate matter in concentrations far exceeding safe limits. Subsurface fires release methane, sulphur dioxide, and carbon monoxide, turning everyday routines into chronic health risks. Respiratory illness is endemic. Skin diseases, eye irritation, and reduced life expectancy are normalised. The built environment, far from being protective, often amplifies exposure. Dwellings are porous, poorly ventilated, and packed densely in zones with no buffer from pollution sources. Public infrastructure clinics, clean water, and sanitation are either absent or overwhelmed. Health becomes spatial: determined by one’s proximity to toxic surfaces, one’s ability to ventilate or isolate, and one’s position within a highly stratified geography of exposure. In this context, the role of architecture remains unclear. It is worth investigating whether material systems, environmental modelling, and passive design strategies can contribute anything meaningful to mitigating this risk.

Instead of resisting movement, we can model architectures that fold, fragment, and migrate systems that breathe with their landscape rather than anchoring against it. This isn’t about rebuilding over fire, but designing with the fire in mind, where uncertainty becomes a design parameter, not a constraint.

1. Government of India. Jharia Action Plan 2009. Ministry of Coal. 2. Malkhandi, Mita. “Displacement and Socio-Economic Plight of Tribal Population in Jharkhand with Special Reference to Jharia Coal Belt.” International Research Journal of Management Sociology & Humanity 9, no. 2 (2018): 96–105. 3. The Dark Earth: Coal Mining and Tribal Lives of Jharkhand. YouTube video, 12:34. June 22, 2024. https://www.youtube.com/watch?v=u1I2PFpHaYE.

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7 km

Fig. 1.15. Relocation Scheme (author)

Fig. 1.16. Relocation Buildings (photo by Stefano Schirato, 2019).

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2.5.3 Economic Dependencies

The economic structure of Jharia is fragile and singularly dependent on coal. Livelihoods revolve almost entirely around the mining economy, with most households tied to it either directly through formal employment or informally through contract labour, scavenging, or support services. This overreliance has produced a brittle ecosystem with volatile wages, unsafe working conditions, and little to no labour protections. Employment is often undocumented, hazardous, and exploitative, offering no path for upward mobility. As a result, families remain trapped in cycles of subsistence without access to diversified income streams. This economic dependency is mirrored in the physical landscape. Informal worker settlements dominate, with little spatial planning or infrastructure to support alternative economies. There are no dedicated zones for education, skill development, light industry, or community-scale enterprise. Markets are scattered and undersupplied; repair workshops, fabrication spaces, and knowledge-sharing hubs are absent. In short, the city produces labour, not economic agency. 1 As part of the design proposal, zoning for economic resilience will be introduced. This includes designated areas for microenterprise, vocational infrastructure, shared manufacturing, and community services spaces intended not just to support living, but to enable working, learning, and evolving beyond extractive dependency.

2.5.4 Displacement and Dysfunction

Attempts to address displacement and economic recovery have largely failed. The Belgaria rehabilitation colony, established by BCCL (Bharat Coking Coal Limited), was intended to resettle families evicted from subsidence zones.2 However, it was designed with no regard for economic sustainability. Residents were relocated to peripheral land disconnected from job sites, markets, or transport. Basic services like water, drainage, and healthcare were either delayed or absent. Most critically, there was no provision for livelihoods, no economic zoning, no skill centres, no transport linkages. As a result, many families either returned to illegal mining areas or fell into deeper poverty. The site became spatially stable but economically void, a clear failure of planning that prioritised land clearance over human survival. The spatial consequence of this economic fragility is a fractured urban form, where informal settlements sprawl chaotically around mining peripheries, with no dedicated space for trade, production, or growth. There are no mixed-use zones, no vocational corridors, and no public infrastructure to support alternative economies. This spatial vacuum reflects a larger planning neglect: the economy is treated as a byproduct of mining, not as a system to be cultivated. In developing a new intervention at an abandoned mining pit, the objective is to learn directly from the failure of Belgaria. Economic zoning must be central to the spatial framework, not an afterthought. Designated zones for agro-based industries, fabrication units, markets, repair services, and vocational training can be embedded directly into the site. These zones would not just enable income, they would anchor people to place with purpose, offering alternatives to extraction through production, repair, reuse, and education. While architectural form alone cannot solve economic collapse, it can create the conditions for economic plurality to emerge.

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Fig. 1.17. Sectional Perptective of Typical vernacular courtyard houses (author)

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2.6 Building Cultures 2.6.1 Vernacular building methods

In tribal regions like Jharia, building culture is not just about making shelter; it is the spatial expression of how a community understands survival, ecology, and time. Unlike urban development models that operate on imported typologies and industrial materials, tribal architecture emerges from embedded systems of local knowledge, where building and living are continuous acts. Construction here is seasonal and ritualistic, aligned with harvesting cycles, weather windows, and community labour. Every built component carries meaning: walls are maintained annually by women in acts of symbolic renewal, while thresholds are treated not merely as entry points but as cultural markers that separate the sacred from the profane. Material choices are not decorative decisions, but climatic responses evolved through centuries of experimentation. This is architecture that doesn’t imitate nature. it participates in it. It metabolises heat, channels water, shelters kinship networks, and maintains continuity. For us, as designers working with computational tools, it is critical not to see these environments as static or backwards, but as adaptive intelligence systems, vernacular operating systems that solve real-world constraints with minimal energy and maximum cultural integrity. The tribal house sits at the intersection of three forces: climate, culture, and function, and its form emerges from their negotiation. Climate dictates materials, orientation, and enclosure strategies; culture informs symbolism, ritual sequencing, and communal use; and function dictates the

interior program and spatial flow. This triadic relationship prevents the architecture from becoming monofunctional or static. A kitchen is not just for cooking; it becomes a seasonal workspace, a storage node, and a place of ritual. The courtyard, shaded in summer and warmed by the sun in winter, serves as a place of rest, processing food, watching children, or meeting guests. This elasticity of use is a powerful contrast to modernist planning’s compartmentalisation. It teaches us that space is not static; it evolves with time, weather, and the bodies inside it. This plan is more than a floor layout; it is a cultural diagram. Rooms are placed in response to prevailing winds and sun path; bedrooms typically orient away from solar exposure, while kitchens are placed near openings for ventilation and smoke egress. Storage is tucked into thermally buffered areas. Doors and windows are minimal, high-set, and deeply recessed to reduce heat gain and cold drafts. Circulation happens around the courtyard rather than through hallways, reducing the spatial footprint while maximising cross-ventilation. Most importantly, the courtyard acts as an anchor, a thermal and social engine that defines not only the environmental comfort of the home but its relational architecture. It is where care happens, both in physical and social terms. For future design systems, this centrality could evolve into multi-scalar courtyards, networked across units, offering decentralised yet interlinked urbanism responsive to both environmental performance and cultural practice.

1. “Tribes of Jharkhand.” Uploaded by Daphneusms. Scribd. Accessed June 25, 2025. https://www.scribd.com/doc/139703199/Tribes-of-jharkhand. 2. Gautam, Avinash. Tribal Housing: A Case Study of Tribes in Jharkhand. M.Arch. thesis, Kansas State University, 2008.

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The sectional drawing articulates how every layer and spatial shift is choreographed to work with the environment. The roof is lifted, allowing air to circulate beneath and draw heat upward and out through a simple yet powerful stack effect mechanism. The thick earthen walls absorb heat during the day and release it slowly through the night, flattening diurnal temperature swings. Overhangs and shading devices extend protection far beyond the envelope. There is no mechanical intervention, and yet these houses remain liveable across extremes. Importantly, the section reveals how people use the space across the day: elders rest under shaded verandas, while children play in the courtyard; clothes, crops, and utensils are set out to dry in solar-rich zones. This spatial choreography is real-time responsive, a kind of temporal programming of architecture that computational tools can simulate and optimise. The courtyard isn’t an aesthetic flourish; it’s a thermal, social, and logistical hub.

Layer by layer, the material system is performing: compacted earth for base insulation and stability, jute and straw for tension and resilience, bamboo as structure and enclosure, and thatch or clay as thermal skin. These materials are locally sourced, recyclable, and low in embodied energy. More importantly, they are maintainable without machinery, relying on human labour, memory, and seasonal rhythm. The act of construction is embedded in social life: everyone builds, everyone repairs, and everyone understands the material’s behaviour. This is architecture as a distributed skill in a system where knowledge is communal, and upkeep is cyclical rather than outsourced. These methods offer a grounded precedent for developing material systems that are not only ecologically responsive but also socially embedded, scalable, and resilient by nature.

1. Dutta, Pallabi, and Md. Mustafizur Rahman. “Learning from the Root – Integrating Tradition into Architecture towards a Self-Subsistent Munda Community.” Conference paper, Khulna University Studies, Shahjalal University of Science and Technology, November 2022. 2. Krümmelbein, Julia, et al. “A History of Lignite Mining and Reclamation in Lusatia.” Canadian Journal of Soil Science 92, no. 1 (2012): 53–66.

The Domain

Fig. 1.18. Construction elements of a vernacular houshold (redrawn from Dutta and Rahman, 2022).

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2.7 Global Lessons

2.7.1 Fly Ash Pressure Grouting: Mitigating Subsidence and

Fires through Void Backfilling

In the room-and-pillar mines of Shaanxi, China, the injection of a fly ash slurry into subterranean voids has been shown to suppress ground subsidence by 75–80%.¹ The technique, which relies on precision control systems to manage the fill material, achieves a compressive strength of 1-2 MPa after 28 days, thereby stabilising the overlying strata. This has resulted in a significant reduction of maximum subsidence, from 2.0m to High Temperature 0.4m. soil heaps Applicability to Jharia: This technology suggests a pathway Day 0 towards establishing the fundamental ground stability required before installing any structures on the open-pit slopes. Filling the subterranean voids could contribute to reducing the risk of slope failure, which is exacerbated by the monsoon climate. Utilising the abundant local supply of fly ash in Jharia is a clear advantage, though a critical line of inquiry would be the development of a localised cement-fly ash composite capable of immobilising the region’s primary soil contaminants, namely arsenic and boron. This case underscores the primacy of pre-emptive ground Stabilizing stabilisation. Filling subsurface voids prior to surface works Ground proves a necessary precondition for subsequent design success. It also illustrates the circular use of local waste Day 28 streams, indicating how Jharia’s fly ash can be repurposed as a core resource for a sustainable restoration model. Beyond void filling, the case points to a concrete research objective: development of tailored composites capable of immobilising site-specific contaminants such as arsenic and boron. In sum, the project frames geotechnical stabilisation and contaminant management as coupled tasks that can be addressed through Fig. 1.19. Abstracted Principles locally sourced, performance-driven materials.

Subsidence

Injected fly ash slurry

¹ Yue Jiang et al., “Mitigating Land Subsidence Damage Risk by Fly Ash Backfilling Technology,” Polish Journal of Environmental Studies 30, no. 1 (2021): 655–61. ² Guozhen Zhao et al., “Ecological Restoration of Coal Mine Waste Dumps: A Case Study of the Ximing Mine in the Arid Desert Region of Northwest China,” International Journal of Mining, Reclamation and Environment 37, no. 12 (2023): 833–55.

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2.7.2 Ximing Mine, China: A Composite Barrier for High-

Vegetation 18%

Temperature Spoil Heaps

The high-temperature spoil heaps at the Ximing Mine (>180°C) were restored using a dual-defence system, which combines a structural-thermal barrier to block oxygen ingress with a biological cover for surface stabilisation.² Through this method, vegetation coverage was increased from 18% to 72% in two years, with long-term stability assured through computer simulations. Applicability to Jharia: This case provides a direct conceptual model for a structure that can control Acid Mine Drainage (AMD) on the pit slopes. An impervious cap could prevent rainwater during the monsoon season from infiltrating the slopes and transporting pollutants into the Damodar River. An investigation into developing an eco-friendly mortar, blending Jharia’s local refractory clays and biochar as a substitute for concrete, could lead to a cost-effective solution that simultaneously forms a stable base for a vegetation structure and controls AMD generation.

Barren spoil heap

Year 0 Vegetation increased 72%

Biological cover

The Ximing Mine demonstrates the value of a multifunctional composite barrier. A single system can deliver an Surface impermeable cap that intercepts acid mine drainage while stabilisation simultaneously providing a stable, bioreceptive substrate for vegetation establishment. The key lesson is that structural Day 28 intervention should not end with engineering stability; it should be configured as an active substrate that initiates ecological recovery. This integrated stance directly informs the present research agenda, in which bio-receptive structures are conceived as living systems that host plants and microorganisms, enable filtration and metabolism, and thereby couple pollution control with long-term landscape rehabilitation. Fig. 1.20. Abstracted Principles ³ Marta Muro Carbajal, “Restauración Geomorfológica e Hidrológica de la Escombrera del Cerco de San Teodoro (Almadén),” in VI Congreso Ibérico de la Ciencia del Suelo (2012). ⁴ Maximilian Schneider et al., “Modelling of the Acid Mine Drainage Generation in Lusatian Post-Mining Pit Lakes Using Sentinel-2 Data,” Minerals 13, no. 2 (2023): 271. ⁵ Peter Stanley et al., “Pit Lake Water Quality Prediction – A Global Review and a Lusatian Case Study,” in Proceedings of the International Mine Water Association Congress (2023): 338–45.

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2.7.3 Almadén, Spain: Geocell Stabilisation of Steep Slopes

At the Almadén mine in Spain, steep 60° slopes of contaminated spoil were stabilised using HDPE geocells.³ The essence of this technique is that the honeycomb-like cell structure confines the soil, preventing erosion, while each cell provides an individual substrate for vegetation to establish. The project not only reduced erosion and pollution but also transformed the site into a tourist asset generating €1.2 million annually. HDPE geocells

Applicability to Jharia: Geocells offer a direct model for a structure that provides both slope stabilisation and a substrate for vegetation. This approach could be particularly effective in preventing the severe soil erosion caused by torrential monsoon rainfall. A promising direction for local adaptation would be to trial different infill mixtures within the cells, combining limestone to neutralise Jharia’s acidic soils with biochar, and planting deep-rooted, native species resilient to both monsoon and dry seasons to identify an optimal, climatespecific solution. Almadén evidences the effectiveness of modular containment at slope scale. Honeycomb geocell systems show how small, repeatable modules can stabilise extensive unstable faces while distributing stresses and anchoring soils. For Jharia, the implication is clear: porosity within bio-receptive structures should operate as functional modules rather than passive voids, gripping substrate and cultivating plants. Each aperture can serve as an independent microhabitat with tailored soil mixes, moisture regimes and species, increasing biodiversity and redundancy. The design principle is therefore a modular ecology, where repeatable units produce large-scale stability and ecological gain through localised rooting, nutrient cycling and succession.

60 Degree

Fig. 1.21. Abstracted Principles

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Lusatia, Germany: Regional-Scale Landscape Engineering

2.7.4

The 1,000 km² lignite mining area of Lusatia, Germany, was transformed into a sustainable landscape of lakes, forests, and farmland.⁴ The strategy’s core was to re-engineer the topography itself by regrading steep slopes to ensure inherent stability, and then covering the area with an engineered soil layer to restore the ecosystem. Consequently, the acidic soil (pH 3.4) was transformed into healthy land (pH 6.8) within five years.⁵ Applicability to Jharia: The approach taken in Lusatia presents a macro-scale alternative to installing individual structures, focusing instead on re-engineering the open-pit slopes’ topography. Regrading steep slopes is the most fundamental method of ensuring structural stability. This newly created, gentler landscape could provide the safest and most expansive foundation for subsequent revegetation efforts and large-scale architectural interventions. Lusatia shifts the problem frame from adding structures onto risky ground to remaking the ground itself. Macro-scale terrain regrading that softens steep slopes provides the most robust foundation for all subsequent interventions. For Jharia, the lesson is to adopt a hybrid strategy: combine localised bioreceptive structures with targeted large-scale earthworks that remove hazards at source. Such reshaping improves geotechnical stability, attenuates runoff, reduces erosion, and simplifies downstream maintenance. Strategic recontouring thereby functions as primary risk abatement, upon which finergrained ecological and programmatic layers can be deployed with greater durability, accessibility and long-term operational viability.

pH 3.4(Acidic)

Year 0

Vegetation rooting

pH 6.8 (Healthy)

Water body stabilised

Year 5 Engineered Soil

Fig. 1.22. Abstracted Principles ³ Marta Muro Carbajal, “Restauración Geomorfológica e Hidrológica de la Escombrera del Cerco de San Teodoro (Almadén),” in VI Congreso Ibérico de la Ciencia del Suelo (2012). ⁴ Maximilian Schneider et al., “Modelling of the Acid Mine Drainage Generation in Lusatian Post-Mining Pit Lakes Using Sentinel-2 Data,” Minerals 13, no. 2 (2023): 271. ⁵ Peter Stanley et al., “Pit Lake Water Quality Prediction – A Global Review and a Lusatian Case Study,” in Proceedings of the International Mine Water Association Congress (2023): 338–45.

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2.7.5 Haller Park, Kenya: Ecosystem-Led Restoration

Haller Park in Kenya was restored from a barren quarry into a coastal forest ecosystem without any external soil importation. Its defining feature is the use of ‘ecological agents’ a pioneer tree species and a soil-processing animal to build a fertile topsoil layer naturally and with minimal intervention.⁶ This process created 30cm of new soil over two decades. Applicability to Jharia: The Haller Park method is focused on maximising the efficiency of phytoremediation on an already physically stable slope. Once the structural integrity of a slope is secured by other means, a living soil layer could be created using local organic matter, such as cow dung compost from the Jharia region. It is plausible that this healthy soil would activate Jharia’s unique microbial communities, in turn promoting the establishment of native flora with the potential to remediate specific heavy metals like lead and arsenic. Haller Park illustrates the power of ecosystem-led restoration. Once physical stability is secured, effort should shift to creating living soil without importing external topsoil. Low-cost, lowinput practices that mobilise local organic matter, such as cattle-manure compost, activate native microbial communities and kick-start nutrient cycling, moisture retention and soil aggregation. The lesson for Jharia is to treat soil biota as primary infrastructure, establishing biological function first so that vegetation can self-organise. This approach provides the substrate on which the Builder guild can establish and improve fertility over time, using green manures, mulch and microbial inocula to accelerate succession and deliver durable ecological gains with minimal ongoing inputs.

Quarry Void

Day 0 Pioneer Plants

Soil Formation Process

Dense vegetation

Millipedes/ termites Restored Ecosystem

Day 28 Fig. 1.23. Abstracted Principles ⁶ Kristen E. Trippe et al., “Co-application of Biochar and Microbial Inoculum to Promote Phytostabilisation of Acid-Generating Mine Tailings,” Applied Soil Ecology 165 (2021): 103962. ⁷ Eden Project, Annual Review 2023-24 (Bodelva: Eden Project, 2024), 8.

Millipedes/ termites

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2.7.6 The Eden Project, UK: Eco-Regeneration Fusing Tourism and Education

Steep clay pit

The Eden Project in the UK, a world-renowned ecological park regenerated from a clay pit with high rainfall, owes its success to its multi-layered drainage system.⁷ This system comprises Clay Pit engineered soils, drainage mats, rock armour for structural support, and culverts. The absence of severe chemical Year 0 contamination was also a key advantage in the restoration process. High rainfall

Applicability to Jharia: The Eden Project offers a compelling vision for how successful architectural intervention can be achieved on a restored site. Its sophisticated multi-layered drainage system, in particular, serves as a crucial reference for managing acid mine drainage and protecting building foundations within Jharia’s monsoon climate. Furthermore, Engineering the unique environmental condition of geothermal heat Phase from underground fires in Jharia presents an opportunity for exploration as a potential energy source.

Drainage mats + culverts Rock armour

Year 4–10

The Eden Project underscores the need for robust engineered ecological infrastructure in high-rainfall settings. Longterm restoration and subsequent architectural development depend on layered water management that attenuates extreme rainfall, protects foundations and treats polluted runoff. The lesson is to design the stabilisation corridor not only as a structural device but also as primary drainage and treatment infrastructure, integrating swales, attenuation ponds and constructed wetlands to regulate flows and improve water quality. When degraded sites are coupled Restored Ecological Park with such technical systems, they can be transformed from liabilities into ecological and economic assets, providing reliable hydrological performance that underpins settlement, Year 10–20 productive landscapes and future building programmes. Fig. 1.24. Abstracted Principles

Engineered Soil

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2.8 Discussion

This analysis seeks to understand the comprehensive issues surrounding coal mining, approaching them from a critical perspective that promotes reader awareness. As has been demonstrated, the abandoned cavities resulting from open-pit mining have the main impact of fracturing the landscape, but they also generate profound socio-economic implications. The central objective of the project is to transform these extensive abandoned and degraded lands into an integrated cycle of environmental remediation and social recovery. As a team, we understand that when intervening in these areas, we face two fundamental challenges. The first is the spatial scale: given that these mining cavities have a radius of approximately 500 metres, we propose categorising the intervention area according to its soil attributes, slopes, water flows and other relevant factors. This characterisation will allow us to define different areas of action, which will determine specific intervention strategies according to their categorisation. The second challenge is the remediation of the contaminated landscape. To this end, knowledge of phytoremediation and soil restoration through strategic associations of plant and tree species is essential. The project will establish a phased remediation system to achieve this objective. As a team, we are aware that an initiative of this magnitude requires sequential and modular implementation, the phases of which will be detailed in later chapters. In conclusion, this thesis poses a dual challenge: to establish a phased socio-environmental remediation cycle that allows the lost ecosystem to be recovered, transforming it into a productive and ecologically functional system.

2.8.1 Hypothesis

With strategic planting techniques and stabilization methods in addition to bioreceptive structure would transform a former mine and dump site will significantly reduce soil erosion, enhance ground stability and soil fertility, activate agricultural use of the restored land, and ultimately drive regional economic recovery.

2.8.2 Research Question

How can these abandoned landscapes be converted into productive systems that restore the environment and generate new housing and economic opportunities? What phytoremediation strategies are effective in converting soils contaminated by mining oxidation processes into substrates suitable for the development of self-regulating living ecosystems? How can an architectural landscape be designed that evolves over time alongside ecological remediation processes? Which plant species are suitable given the geology and climate of the mining site?

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Fig. 1.25. Proposed Remediation cycle

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2.8.3 Bibliography

The Coal Mining Life Cycle. Mining for Schools. Accessed June 25, 2025. https://miningforschools.co.za/lets-explore/ coal/the-coal-mining-life-cycle.

Central Mine Planning & Design Institute (CMPDI). Annual Report. 2021.

Gupta, Shiv Kumar, and Kumar Nikhil. Ground Water Contamination in Coal Mining Areas: A Critical Review. 2016.

Jharkhand Pollution Control Board. Annual Report. 2022.

Central Institute of Mining & Fuel Research. Research Highlights. 2019.

Greenpeace India. Airpocalypse IV: Assessment of Air Pollution in Indian Cities. New Delhi: Greenpeace India, 2020. https://www.greenpeace.org/india/en/story/7764/ airpocalypse-iv-assessment-of-air-pollution-in-indiancities/. United Nations Framework Convention on Climate Change (UNFCCC). The Paris Agreement. 2015. https://unfccc.int/ process-and-meetings/the-paris-agreement/the-parisagreement. NITI Aayog. India’s Updated Nationally Determined Contributions (NDCs). New Delhi: Government of India, 2021. https://www.niti.gov.in. IEASRJ; Times of India. [No additional bibliographic details provided.

10. Riyas, Moidu Jameela, Tajdarul Hassan Syed, Hrishikesh Kumar, and Claudia Kuenzer. “Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data.” Remote Sensing 13, no. 8 (2021): Article 1521. https://doi. org/10.3390/rs13081521.mdpi.com. 11. IEASRJ; Chapman University. “Underground Burning of Jharia Coal Mine (India) and Associated…”. [No additional bibliographic details provided.]

12. Global Bihari. “Considerable Reduction in Surface Fire Area in Jharia Claims Coal Ministry.” Accessed June 2025. https://globalbihari.com/considerable-reduction-insurface-fire-area-in-jharia-claims-coal-ministry/. 13. Ministry of Coal, Government of India. “Jharia Master Plan: Coal Ministry Efforts Bring Down Surface Fire Identified from 77 to 27 Sites.” Press Information Bureau press release, September 25, 2023. Accessed June 25, 2025. https://www.pib.gov.in/PressReleaseIframePage. aspx?PRID=1960543. 14. Selvi, V. A., et al. “Impact of Coal Industrial Effluent on Quality of Damodar River Water.” Indian Journal of Environmental Protection 32, no. 1 (January 2012): 58–65. 15. Sharma, A. K., B. P. Singh, and C. L. Prasad. Degradation of Soil Quality Parameters Due to Coal Mining: A Case Study of Jharia Coalfield. CORE PDF. Accessed June 25, 2025. https://core.ac.uk/download/pdf/188610081.pdf. 16. Saini, Varinder, R. P. Gupta, and Manoj K. Arora. “Environmental Issues of Coal Mining – A Case Study of Jharia Coal-Field, India.” Energy Procedia 90 (2016): 634–641. https://www.researchgate.net/ publication/291102685_Environmental_issues_of_coal_ mining_-_A_case_study_of_Jharia_coal-field_India. 17. Government of India. Jharia Action Plan 2009. Ministry of Coal. 18. Malkhandi, Mita. “Displacement and Socio-Economic Plight of Tribal Population in Jharkhand with Special Reference to Jharia Coal Belt.” International Research Journal of Management Sociology & Humanity 9, no. 2 (2018): 96–105. 19. The Dark Earth: Coal Mining and Tribal Lives of Jharkhand. YouTube video, 12:34. June 22, 2024. https://www.youtube. com/watch?v=u1I2PFpHaYE. 20. “Tribes of Jharkhand.” Uploaded by Daphneusms. Scribd. Accessed June 25, 2025. https://www.scribd.com/ doc/139703199/Tribes-of-jharkhand. 21. Gautam, Avinash. Tribal Housing: A Case Study of Tribes in Jharkhand. M.Arch. thesis, Kansas State University, 2008.

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22. Dutta, Pallabi, and Md. Mustafizur Rahman. “Learning from the Root – Integrating Tradition into Architecture towards a Self-Subsistent Munda Community.” Conference paper, Khulna University Studies, Shahjalal University of Science and Technology, November 2022.

34. Singh, Abhay Kumar, G. C. Mondal, Suresh Kumar, T. B. Singh, B. K. Tewary, and A. Sinha. “Major Ion Chemistry, Weathering Processes and Water Quality Assessment in Upper Catchment of Damodar River Basin, India.” Environmental Geology 54, no. 5 (2008): 745-58.

23. Krümmelbein, Julia, et al. “A History of Lignite Mining and Reclamation in Lusatia.” Canadian Journal of Soil Science 92, no. 1 (2012): 53–66. 24. Lausitz & Central German Mining Company (LMBV). Mine Rehabilitation in Germany: Example LMBV. Senftenberg, 2023. 25. Ram, L. C., and R. E. Masto. “Fly Ash for Soil Amelioration.” Earth-Science Reviews 128 (2014): 52–74. 26. Jiang, Yue, et al. “Mitigating Land Subsidence by Fly-Ash Backfilling.” Polish Journal of Environmental Studies 30, no. 1 (2021): 655–661. 27. Mishra, D. P., and S. K. Das. “Physico-Chemical Properties of Talcher Fly Ash for Stowing.” Materials Characterization 61, no. 11 (2010): 1252–1259. 28. Siachoono, Stanford M. “Land Reclamation in Haller Park.” International Journal of Biodiversity and Conservation 2, no. 2 (2010): 19–25. 29. Trippe, Kristen E., et al. “Phytostabilisation of Acid Tailings with Biochar and Microbial Inoculum.” Applied Soil Ecology 165 (2021): 103962. 30. Coal Story. story.

https://88guru.com/library/chemistry/coal-

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III

METHODS Reimagining and remediating mine lands can be looked at as a global model with solutions that could lie in governance or low- effort restoration or planting measures but the research aims to investigate and respond to its immediate complex parameters. A thorough investigation of the complex land morphologies and remediation methods answering multiple environmental factors and social dependencies requires a data-driven methodology to provide a bottom-up solution. This would inform climatic response, intervention strategies, building morphologies, planting strategies and spatial planning. The research is conducted in two phases: The MSc phase focuses on multiple network strategies, bioreceptive structure designs and development of bio-receptive materials; while the MArch phase undertakes expanded development of Architectural typologies in lines of the established strategies The research undertakes data collection to inform its experiments at different scales. The quantitative information about the altered minescapes and land morphology, spatial data is procured using Geographic Information Systems(GIS) tools. Numerous published environmental research, scientific studies, environmental research, scientific studies, government plans, articles are sources to understanding social issues and environmental impacts. Local surveys and horticulture data helps in identifying the native plant types. Precedence of global mine sites and reclamation efforts are guides to remediation strategies and models for economic resilience and project timeline. Land stabilisation, settlement clustering, bioreceptive morphologies and materials along with planting systems are prioritized. Data from various environmental analysis informs the agent based generative stabilization network. Multiobjective optimization methods are used to optimize networks, productive landforms and building morphologies. Planting methods are evaluated by ecological modelling tools to determine improved plant and soil conditions. Environmental optimization and structural analysis is conducted for various scales using Finite Element Analysis(FEA) and environmental analysis tools. Prediction of allocation of built structures, planting patterns and settlement clustering onto a network is conducted using unsupervised machine learning algorithms. Physical tests are conducted on various material experiments. In this way, the research employs multiple tools and methods to address building morphological and landscape interventions. 54

Methods

3.1 The System : Environment to Data

3.1.1 Terrain into patches

An intelligent environment is built in this research based on the integration of all simulations with fragmented geological information in this research. Contours, geologic and soil map information are generated from Landsat 7 and Sentinel-2 satellite data of USGS and Copernicus respectively, and divided into 5x5m uniform ‘patches’. Each patch functions as an independent data storage, containing overlaid quantified data layers such as land subsidence, rock formation, etc., which are analysed using Slope, Satellite data(e.g., InSAR). This process translates physical space into digital data that computers can ‘recognise’ and ‘react’ to. The intelligent environment thus established is the crucial first step in ensuring that all subsequent analyses and design generation processes based on eliminates subjective bias. 3.1.2 Agent-Based Network Generation

An Agent-Based Model, a bottom-up approach, is utilised to explore a macro structure as stabilisation corridor based on an established environment. This methodology allows complex macro patterns to emerge, where numerous agents interact by following simple local rules without central control. Agents move perceiving unstable areas which have severe ground subsidence or high slope angle as attractors, rock formation as repulsors. These collective interactions efficiently enable them to explore vast amounts of solution space that is impossible to search manually for a human designer. Agents gradually search for and enhance optimal routes using collective intelligence that converges into a single organic network encompassing the entire complex territory. The core of this method, establishing a generative process that reveals itself with an optimised form, rather than directly designing the final form. Fig. 3.1. The System Methods 55

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3.1.3 Selection via Multi-Objective Optimisation

Thousands of network alternatives generated by Agent-Based Modelling (ABM) are quantitatively evaluated and selected through a Multi-Objective Optimisation (MOO) process. Conflicting performance objectives such as maximising stability, minimising total length, and minimising average inclination are considered simultaneously. Rather than a single “perfect” solution, this process derives from the ‘Pareto front’, a set of optimal trade-offs in which improving one objective necessarily degrades another. Finally, the most suitable network within this portfolio is selected according to the project’s core values (e.g., giving top priority to stability). This ensures a transparent and rational decision-making process based on data, rather than the designer’s subjectivity. 3.1.4 Defining Land Use through Clustering

Building on the optimised stabilisation corridor as a macro framework, the specific use of each patch within the intervention area is defined and translated into a practicable spatial plan. The aim is to group numerous potential building plots, identified from corridor-wide risk data, into meaningful residential compounds, or ‘clusters’. To this end, the K-Medoids clustering algorithm, an unsupervised learning method, is adopted. Unlike K-Means, which computes a centroid that may fall in empty space, K-Medoids selects a cluster centre (medoid) from among the actual data points. This offers a decisive advantage in the present project because each cluster centre is guaranteed to be a buildable ‘Foundation Group’ located on the stabilisation corridor itself. As a result, all dwellings within a cluster can reach the centre via real routes without unrealistic detours, securing practical accessibility. Through this clustering process, each patch acquires a more specific programmatic identity. For example, some patches are designated as ‘simple stabilisation structures’ for planting only, others become ‘structures combined with buildings’, and still others are defined as ‘potential building plots’ for future community expansion or as ‘community spaces’ acting as cluster centres. This constitutes the critical step that translates the macro network plan into concrete spatial programmes that accommodate human life and activity. Fig. 3.4. The System- Optimization 56

Methods

3.2 The Structure : Generative Design

3.2.1 Defining Functional Guild

To address complex ecological restoration problems, a paradigm shift is adopted in which plants are classified not by species or morphology but by functional role. This is analogous to assembling a team of specialists to solve a defined problem. The methodological foundation is the Jharia_plnats_database. csv, compiled through a literature review of more than 200 native species from the Jharia region. The database converts each species’ attributes into computable parameters that can be referenced directly by an intelligent algorithm. Each row represents an individual species, and each column defines key attributes essential to algorithmic decision-making as follows: Basic information (Name, Type): specifies the plant’s name and growth form. 1.

4. 5.

Functional classification (Synergy_Group, Strategic_Guild): Synergy_Group denotes fine-grained ecological roles, while Strategic_Guild indicates the macro functional group defined in this study. Environmental tolerances (Sun_Range ~Subsidence_ Tolerance): core filters for assessing survivability on a given patch, expressed quantitatively or as 1/0. Physical properties (Root_Thickness_m, Growth_ Radius_m): used directly to determine pore size in bioreceptive structures and planting density. Ecological strategy (Dominance): an index of early growth rate and reproductive vigour. On this basis, four core functional guilds are defined: Anchor, which physically secures unstable soils; Detox, which purifies heavy-metal contamination; Builder, which increases fertility on degraded land; and Support, which promotes the growth of other guilds and enhances biodiversity. This functional approach provides the foundation for tailored planting strategies that respond precisely to diverse environmental challenges.

3.2.2 Intelligent Planting Algorithm

A self-sustaining plant ecosystem is meticulously crafted for each specific area, taking into account unique environmental factors such as soil contamination, water flow, and rock formations. The algorithm developed in C# filters survivable plant species in the patch in question in the database. Based on each soil patch’s unique environmental data (contamination level, soil moisture, and underlying rock), the optimal plant assemblage is then designed automatically. Next, the algorithm diagnoses the patch’s most pressing issue (e.g., severe contamination) and dynamically increases the proportion of the guild most needed (e.g., Detox). Finally, the assemblage is configured according to a “diversity-first, dominant-species-complement” principle. This strategy prioritising the broadest possible range of species for longterm ecological resilience while filling gaps with earlyestablishing dominant species to secure stability emulates the natural succession processes of ecosystems. Fig. 3.5. The Ecological Logic- Methods 57

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3.3 The Material 3.3.1 Material Formulation The project began with an investigation into the vernacular material culture of Jharia, where vernacular buildings heavily utilize mud walls made of a composite of clay, soil, water, and organic plant fibres such as hemp. Building on this tradition, the base mixture was formulated as a 3D-printable composite. Stabilizers like lime and fly ash, both waste products of mining landscapes, were incorporated to provide cementitious strength and durability, a viable eco-friendly substitute for concrete. To make the material ecologically responsive, biochar and vermiculite were used, enabling it to retain moisture and create microenvironments supportive of microbial and vegetative colonization. As the site is hazardous and not suitable for hand building, the mixture was specially prepared for robotic arm extrusion. But the initial preparations lacked sufficient flow cohesion with mechanical stress. This deficiency was eased with the introduction of 5% alginate and 5% locust bean gum as biopolymer binders. This innovation settled rheology and ensured uniform extrusion in robot testing, where scale models of 1:10 were extruded successfully on planar surfaces. The ultimate composition material balanced structural capacity with ecological acceptability, harmonizing ground stabilization and environmental restoration on a long-term basis. 3.3.2 Testing and Optimization

To determine the performance of the material system, controlled testing was carried out. Tests on syringe and auger machines for early-stage extrudability, flowability, and the material’s capacity to sustain deposition under constant pressure were evaluated. Post-extrusion stability determination, mechanical and environmental testing was conducted: Compression Test tested the structural loads printed specimens could withstand to ensure if the material was capable of withstanding structural demands. Shear Test checked the lateral stability and the resistance to failure along planes of weakness, which validated its use on unstable and steep slopes. Water Retention Test assessed the capacity of the composite to retain and absorb water, a fundamental parameter for supporting biological growth. pH Test found the chemical environment of the material to ensure that it is neutral and apt for microbial colonization. With comparative testing across a range of mixtures, the experiments identified a balanced point where structural strength (from lime and fly ash) combined with ecological receptivity (from biochar and vermiculite). This two-fold verification ensured the material was both a feasible stabilizer and regeneration medium, ready for application via robotic arm manufacturing in hazardous environments. 58

Fig. 3.4. The Material- Methods

Methods

3.4 The Structure : Generative Design

3.4.1 Data correlation

Risk analysis, Slope angles, data driven zoning of the structure, and planting allocation form the base of the bio-receptive structure experiment. These data are used to inform the location, extent of the structure. 3.4.2 Form-Finding

The form-finding experiments evaluate material behavior for making of structure that has multiple performative purpose. Material and planting interplay is initially tested by different methods and the optimum is taken forward. The reaction diffusion algorithm offered various planting densities and material deposition that are tested within the structure, followed by a method that prioritized planting growth radii which informed the material deposition, and finally the variation of the pockets informed by structural stressline generation under necessary loading. These three methods of form finding are discussed and evaluated for the final morphology. 3.4.3 Finite Element Analysis

Finite Element Analysis (FEA) is employed to assess the structural performance of the structure that needs to facilitate earth retention, architectural loading and anchoring. Material properties recorded from the material experiments are integrated in Karamba3D to inform initial performative thickness and development of the base geometry. Force flow, principal stress and principle stress lines are evaluated for iterations and final morphologies. This helped to identify redundant material zones to then subtract material forming places for planting pockets. These modifications are again informed to ensure load transfer. The structure adapted to various ground conditions- each offering a unique slope condition, scale of structure, planting allocation, leading to variation informed by this generative workflow. 3.4.4 Multi-objective evolutionary optimization

Multi-objective evolutionary optimization is undertaken using Wallacei to evaluate opposing performance objectives for the structural scale evaluating numerous iterations of morphological variations. For the structure, objectives that helped in maximizing planting pockets, while maintaining structural performance were assigned. 3.4.5 Robotic Toolpath

The structure is also designed to be 3d printed on different terrain conditions hence, a robotic toolpath is generated using Ai- build platform ensuring that the geometry is printable in various slope conditions and various non planar slicing techniques control the printing angles in relation to robotic arms. Limitations such as length of the structure, printable angles and overhangs along with opportunities offered by the 3d printing techniques informed the form-finding of the geometry.

Fig. 3.5. The Structure- Methods 59

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Fig. 4.3. Process Model

Research Development

RESEARCH DEVELOPMENT This chapter documents in detail the process by which the methodical framework as defined in the previous chapter ‘CHAPTER 03: METHODS’ is applied and developed within the practical research procedure.It is a process of verifying and advancing the abstract methodology through concrete data and experiments, and it constitutes a key stage in establishing the logical foundation of the final design proposal. The chapter is organised into five development phases: decoding of the system, establishment of ecological logic, preliminary research for settlement, material performance evaluation, and structural form exploration. Within each phase, work is conducted on an intelligent environment: risk modelling is used to derive interventionpriority zones; plant-guild strategies and data-layer rules are developed; scenarios are devised according to patchselection criteria; the printability and performance of bioreceptive materials are tested; and structural forms are explored with regard to interactions between materials and planting. All of these processes are closely interlinked and, through a feedback loop between data-driven analysis and physical experimentation, evolve into increasingly refined and realisable design solutions.

Fig. 4.1. Unstable areas and degraded soil.

The aim of our project is not only to remedy areas degraded by mining, but also to open up new opportunities for the land and the people who live on it. To achieve this, we propose a stabilisation corridor, a continuous ecological strip that connects the most unstable areas, makes them safe and, at the same time, delimits new spaces for cultivation and community life. This system heals the land and at the same time lays the foundations for future growth. Fig. 4.2. Stabilization corridor.

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4.1 The System : Decoding

4.1.1 Risk Modeling

The first stage of system development involved the ‘decoding’ of complex and heterogeneous physical characteristics of terrain into quantitative and systematic data. This stage established the basis of the intelligent environment to be used in subsequent design phases, with the aim of building a comprehensive risk model by superimposing multiple data layers. First, we analysed the United States Geological Survey (USGS) Digital Elevation Model (DEM) data to generate a slope (S) map for the entire site, which visualises the primary risk factors for soil erosion and slope failure. Next, we conducted time-series analysis of InSAR satellite data to capture subtle, otherwise imperceptible ground movements, and from this produced a subsidence (U) map that serves as a key indicator of potential void risks caused by underground fires or abandoned mines. In addition, by analysing geological maps and satellite imagery, we extracted the distribution of bedrock (R), which constrains construction and vegetation, as well as the altitude (A) at each location. To integrate these individual data layers, we developed a parametric risk formula applied to each patch (a 5 × 5 m grid cell):

Risk = S + U + A − 0.3*R + 1.2*SU Each variable was normalised to a value between 0 and 1. Notably, because bedrock (R) is geologically stable, it acts as a risk-reducing factor (−0.3*R), although its influence was limited in view of excavation difficulty. An interaction term (1.2*SU) was also weighted to reflect the synergy whereby risk rises sharply when steep slopes coincide with subsidence. Through this process, the corridor to be selected at a later stage is expected to be transformed from a simple 3D model into a quantified ‘Risk Landscape’ in which each location has a unique risk value. 62

Research Development

Fig. 4.4 . Overlapping of layers for corridor system experiment. (author) 63

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4.1.2 Priority Intervention Zones

The ‘Risk Topography Map’ from the previous risk modeling used as a strategic tool to distribute limited resources in the most efficient way. It is impossible to manage every unstable area simultaneously, prioritising intervention on areas containing the most urgent and significant risks. In this stage, patches whose risk values fall within the top 10% on the Risk Topography Map are extracted as hotspots. GIS spatial analysis tools are used to examine the spatial distribution and density of these hotspots. Clusters of adjacent hotspots are regarded as composite risk zones, which can pose a greater systemic failure risk than the sum of individual points. Through this analysis, dispersed risk information across the site is distilled into a set of core Priority Intervention Zones. These zones are not merely collections of high-risk spots; they are strategic anchors where stabilisation efforts can produce the greatest cascading benefits, for example the upper reaches of spoil heaps that influence catchment hydrology or boundary areas where multiple subsidence zones converge. The outcome of this stage does not predetermine the precise route of the final stabilisation corridor. Rather, clear target locations that the agent-based system must traverse and connect in the subsequent design-development phase are established. This lays the groundwork for a systematic, datadriven approach that addresses the most critical problems first while minimising subjective bias.

Research Development

Fig. 4.5. Concept diagram of patch classification. (author)

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4.2 Ecological Logic : Guild Strategy

4.2.1 Corridor Guild Strategy

The first step to solving the complex ecological-restoration challenge in the devastated mining region is to establish a systematic database grounded in an in-depth understanding of the local ecology. To this end, we conducted an extensive literature review of more than 170 native plant species in the Jharia region and created the Jharia Phytoremediation Plant Database. Beyond names and forms, the database contains quantitative and qualitative attributes that can be directly referenced by the ecological-restoration algorithm. For each species, we recorded over 15 key attributes, including slope range, pollution tolerant, and root thickness. Analysing this database, we reclassified all species into four functional guilds, defined by the core role each plant can play in ecosystem restoration, rather than by traditional taxonomic categories. This guild framework provides a strategic blueprint for ecological restoration. Anchor species are deployed at the frontline of the intervention-priority zones identified during the system-decoding stage. Species with high Subsidence_ Tolerance and greater Root_Thickness_belong here; their robust root systems bind soil particles, forming the first line of defence against slope failure and creating a stable base for subsequent guilds, these are called stabilising plants. The Detox guild is concentrated in areas burdened by heavy-metal accumulation from mining activities. Species recorded with Pollution_Tolerant = ‘1’ constitute its core, removing toxic substances from the soil to “treat” the ground and enable a long-term transition to safe agriculture and settlement, these plants area classified as phytoremediation plantas. The Builder guild is placed on spoil heaps and severely eroded soils to “make the ground”. Predominantly nitrogen-fixing legumes, these species enrich the substrate with organic litter, gradually converting barren ground into microbially active, fertile soil, enriching soil plants. Finally, the Support guild is interplanted with the other three to raise overall system health and resilience. It includes species whose Synergy_ Group is classified as “Canopy” or “Ornamental”, moderating microclimates and attracting beneficial insects and microbes to increase ecosystem complexity and stability, called diversity plants. In this way, the entire corridor is conceived as a large, living organism in which each segment receives a dynamic mix of guilds tailored to its specific problems. 66

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Fig. 4.5. Concept diagram of the classification of plant types according to the intervention patch. (author)

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Fig. 4.6. Identified Plant Categories

Stabilising Plants

Phytoremediation Plants

The first challenge is to prevent the soil from sliding or collapsing. Stabilising plants have deep, fibrous root systems that penetrate several metres into the subsoil. These roots increase soil cohesion, they strengthen the soil’s ability to hold together in the face of external forces. In practice, they function as a living mesh, binding soil particles together, and as these roots grow so deep, they cause the soil layers to bind together. This effect reduces and controls the risk of subsidence (land sinking). Soils resulting from mining degradation are soils resulting from excavation and are therefore loose, i.e. weakened. In short, these plants not only fulfil an ecological function, but also act as a natural geotechnical reinforcement.

Chemical contamination is another major problem in mining soils. Phytoremediation plants have the ability to act directly on heavy metals and other contaminants. There are two main mechanisms: Phytostabilization: the roots immobilise the metals, fixing them in the soil near the plant. This prevents the contaminants from moving with the water or spreading to other areas. Phytoextraction: some species absorb heavy metals through their roots and accumulate them in their leaves and stems. This does not ‘eliminate’ the contamination, but it concentrates it in the plant’s biomass, allowing it to be controlled and safely removed. Both processes turn these plants into living filters. Although the soil remains contaminated, the pollutants are isolated and their spread is halted. This mechanism is crucial in places with soil contaminated by mining, where the migration and/or expansion of metals prevents the soil from being used to grow plants.

Research Development

Soil Enriching Plants

Biodiversity Plants

Mining not only pollutes, but also impoverishes the soil, leaving it without nutrients. That is why it is important to include enriching plants in the system, whose function is to restore fertility. Many of them belong to the legume family (such as clover, peas or beans), which have bacteria that live in their roots. These bacteria carry out a process called biological nitrogen fixation, capturing nitrogen from the atmosphere (a gas that plants cannot normally use) and transforming it into compounds that enrich the soil, making it available to other plant species. In addition, when these plants die and decompose, their biomass contributes organic matter to the soil, improving its structure, water retention capacity and nutrient availability. In other words, they act as a fertility restoration system: first they prepare the soil to receive life, and then they allow agricultural or forestry species to thrive.

The last type of plants has a more ecological role, seeking to restore biological diversity in a degraded ecosystem. These species tend to form broad canopies, produce flowers and fruits, and thus attract a wide variety of organisms, pollinating insects, birds, small mammals and even natural predators that balance the food chain. In ecology, these food chains connect plants, herbivores, carnivores, and decomposers. By establishing these networks, biodiversity plants create microhabitats, small spaces within the landscape that serve as refuge or food sources for different species. This not only increases biological richness, but also restores ecological resilience to the territory. The more diverse a community of living beings is, the more capacity it has to resist external disturbances, such as pests, droughts or climate change. In a mining context, these plants are essential because they reintroduce ecological complexity into impoverished landscapes.

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4.2.3 Plant Selection

Considering the soil conditions in coal mine sites, certain plant species were carefully selected based on the specific metal contaminants identified in soil studies. These will be part of phytoremediation cycles, where the plants are extracted and replanted until the soil is remediated. In addition, early-stage plants that help improve nitrogen and phosphorus contents that are essential for enriching the soil, were also identified. These are locally available and thriving species some of which have been mapped as per surveys conducted in Jharia’ mine vicinity.1

1. Mukhopadhyay and Subodh Maiti, “Phytoremediation of Metal Mine Waste,” Applied Ecology and Environmental Research (Budapest: Alöki Kft., 2010), figure 1, accessed via aloki.hu.

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Soil Enriching Plants

Phytoremediation Plants

Fig. 4.7. Selected local plants

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4.2 4 Plant Characteristics

Table. 4.. Plant species and their classification. (various sources compied by AI)

Research Development

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4.3 The Settlement : Pre-Study

4.3.1 The Program

Fig. 4.8. Programme Derivation

The programme in this project suggests a multilayered evolutionary framework that is not a traditional architectural master plan, but supports and catalyses the ‘making ground’ process. It focuses on a pre-architecture phase that secures ecological and physical stability prior to building, with the aim of laying the groundwork for subsequent architectural interventions. Beyond the mere provision of housing, the programme advances a strategic agenda to address the complex socio-ecological challenges of the Jharia area. First, the core of the programme is the establishing ecological restoration infrastructure. This includes seed banks and greenhouses for conserving and cultivating native species, on-site fabrication facilities for producing and processing bioreceptive materials, and an ecological research laboratory to monitor and study the restoration process. These facilities serve as operational hubs, support a circular economy model, and ultimately underpin the creation of new green jobs. Second, the programme organises productive landscapes. The processes of soil remediation and stabilisation are configured to generate economic value. For example, phytoremediation crops are cultivated for biofuel or industrial fibres, terraced agriculture or silviculture is introduced on stabilised slopes, and plant-based water-quality systems are implemented, such as constructed wetlands informed by hydrological analysis, to secure clean water while providing habitats. Third, residential scenarios are redefined. Given the site’s harsh conditions and latent risks, large-scale direct habitation may be unrealistic. Two scenarios are therefore explored. One places only minimal, special-purpose accommodation within the restoration site for managers and researchers. The other establishes housing in a safe buffer zone adjacent to the site and couples it to the economic and ecological benefits of the restored land. This approach learns from the shortcomings of uniform, state-led resettlement schemes by considering social and ecological restoration together. In this way, the programme understands architecture not as a final objective but as an outcome of a process that heals the ground and forges new socio-ecological relations. The programme framework proposed at this research stage constitutes preliminary work that sets the conceptual groundwork, and it will be developed in the subsequent M.Arch phase into more advanced spatial planning and concrete architectural design.

Research Development

Fig. 4.9. Diagram showing the classification of patches according to their function.

4.3.2 Patch Selection and Cluster Criteria

Chapter 4.1.2 defines ‘Priority Intervention Zones’ as “critical instability zones” crucial for stabilisation. This process involves identifying these zones and then utilising the analysis to locate “strategic implementation zones” where the programme can be implemented. In other words, site selection for settlement and associated infrastructure reverses the risk map in order to screen, in a systematic manner, for stable ground with latent potential. The primary criterion is ground stability. Among the areas modelled in 4.1.1, patches in which stabilisation has been achieved are given first priority. This is a necessary precondition for structural safety and long-term residential security. The second criterion is coupling with productivity. Selected stable zones should adjoin prospective productive

landscapes, such as agriculture, forestry and water-treatment installations, so that habitation, economic activity and ecological function can be closely integrated. The final criterion is expandability. Preference is given to locations with the potential for initial clusters to extend progressively over time into surrounding areas that subsequently become stabilised. Applying these layered criteria identifies multiple candidate cluster sites along the stabilisation corridor. Each cluster is conceived not as a simple housing compound but as an integrated operational hub that combines ecological restoration infrastructure, production facilities and minimal residential accommodation.

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Fig. 4.10. Photo of Material experiments 76

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4.4 The Material: Performance 4.4.1 Introduction

Mud has long served as a traditional construction material in the region, with local communities well-versed in its handling, application, and maintenance. Its widespread availability and cultural familiarity make it an ideal foundation for adaptive, site-specific building practices. In this context, mud refers to a specially designed mix of local soils combined with stabilizers, fibers, and other additives specifically chosen for the site. These mixtures help with insulation, control moisture, adapt to different structures, and fit well with the environment. Materials such as fly ash, biochar, rice husk, and waste from local incinerators enhance the properties of the mud and support recycling by utilizing local waste. Recent advancements in 3D printing with earthen materials have expanded the architectural potential of mud. Research suggests that properties such as granularity, plasticity, shrinkage, and rheological behaviour can be controlled to optimize extrudability and buildability.³ The inclusion of binders and natural fibres, such as straw, sisal, and hemp, enhances shape retention, increases tensile strength, and reduces cracking.⁴ In regions such as Jharia, these systems provide multiple benefits. They protect communities from persistent environmental hazards and support ecological restoration through carbon sequestration and the absorption of pollutants. As the construction sector strives to strike a balance between performance, climate resilience, and ethical material sourcing, reformulated earth-based composites are likely to play a pivotal role in fostering more regenerative and adaptive built environments.

1. Flora Faleschini et al., “Sustainable Mixes for 3D Printing of Earth-Based Constructions,” Construction and Building Materials 398 (2023): 132496, https://doi.org/10.1016/j.conbuildmat.2023.132496 2. Yameng Ji, Philippe Poullain, and Nordine Leklou, “The Selection and Design of Earthen Materials for 3D Printing,” Construction and Building Materials 404 (2023): 133114, https://doi.org/10.1016/j. conbuildmat.2023.133114 3. Tashania Akemah and Lola Ben-Alon, “Developing 3D-Printed Natural Fiber-Based Mixtures,” in ICBBM 2023: Bio-Based Building Materials, ed. Sofiane Amziane et al. (Cham: Springer, 2023), 555–572, https://doi.org/10.1007/978-3-031-33465-8_42 4. Ji, Poullain, and Leklou, “The Selection and Design of Earthen Materials.”

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4.4.2 From mining waste to printable matter

Traditional construction methods fail in geologically unstable areas like Jharia, where unstable ground, toxic gases, and underground fires make it unsafe for people and heavy equipment. In such sites, conventional methods are not only inefficient but dangerous.¹ Workers face toxic fumes, unstable terrain, and extreme heat from coal fires. The challenges are both structural and human. A new building method is needed, one that keeps people out of harm, adapts to shifting terrain, and ensures accuracy and repeatability. Robotic-assisted construction offers such a path.² Using a compact, programmable robotic arm, modular components can be fabricated, assembled, and deployed. Mounted on a stable base or mobile unit, the robot builds shelters, walls, or remediation surfaces with precision even in chaotic ground.² This study proposes a robotic methodology that extrudes and assembles modular elements along programmed tool paths.³ A compact arm deposits material layer by layer under precalibrated settings. The approach draws on precedents in remote fabrication, emergency sheltering, and digital earth-building.³ Its novelty lies in applying mud-based composites under extreme conditions. The goal is a safe, localized, and repeatable system that:

Removes human exposure to toxic or unstable sites Delivers consistent, high-quality modules Enables controlled use of experimental, bio-receptive materials Functions on- or off-site, with simple transport and setup The objective is to validate a robotic workflow using mudbased composites for thermal resistance and plant growth. It builds modular elements for rapid deployment and can be operated locally by a small, minimally trained team.

1. Ananya Deshpande, Burning Ground: Infrastructure and Survival in Jharia’s Coalfields (New Delhi: Earthline Press, 2021), 88–90. 2. Chea, C. P., Bai, Y., Pan, X., & Arashpour, M. (2020). An Integrated Review of Automation and Robotic Technologies for Structural Prefabrication and Construction. Journal of Engineering Safety and Environment, 2(2), 81–98. 3. Ardiny, H. (2017). Functional and Adaptive Construction for Rescue: An Analysis of the Approach Using Autonomous Robots. EPFL.

Research Development

Fig. 4.11. Opportunities to harness waste materials (author)

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3.3.1 Existing soil condition

Fig. 4.12. Soil Samples of various mines in Jharia (adapted from Springer, 2021)

1. Maiti, S. K. Ecorestoration of the Coalmine-Degraded Lands: Indian Scenario. New Delhi: Springer, 2021. 2. Singh, G., and B. K. Tewary. “Impact of Coal Mining and Mine Fires on the Local Environment in Jharia.” Environmental Monitoring and Assessment 186, no. 10 (2014): 5955–5964. https://doi.org/10.1007/ s10661-014-3824-7 3. Sarkar, D., and R. Rano. “Soil Physical Constraints in Degraded Landscapes of Eastern India.” In Soil Degradation and Restoration in India, edited by A. Bandyopadhyay et al., 131–148. New Delhi: Springer, 2020. 4. Based on site soil testing conducted by the research team in Barrare, Jharkhand (2024).

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The land in Barrare, located adjacent to the Jharia coalfields, exhibits clear signs of ecological exhaustion.⁴ Decades of mining activity have stripped the topsoil of essential nutrients, resulting in high acidity (pH 4.5–5.5), low organic matter, and deficient levels of macronutrients such as nitrogen, phosphorus, and potassium.⁵ The texture class in this region largely aligns with sandy-skeletal and coarse-loamy profiles characterised by high gravel content and minimal clay, offering poor water retention and low binding capacity.⁶ This compromises both vegetative growth and foundational loadbearing potential for architecture.

Fig. 4.13. Mine pits at showing ground strata ((photo by Peter Caton/Greenpeace, adapted from Mongabay).

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Additives and Stabilisers

Biochar (5–10%)

To engineer a composite material capable of meeting both structural and ecological performance criteria, a suite of natural and industrial additives has been proposed. These stabilisers are introduced in varied combinations across test groups, each selected for its ability to enhance specific material properties while remaining contextually relevant to the Barrare site.

Introduced as a functional carbon additive, biochar improves internal porosity and moisture buffering.¹¹ Its porous matrix fosters microbial growth, potentially transforming the material into a bioreceptive substrate. In future studies, this quality could support the colonisation of beneficial organisms or vegetation, linking architectural material with regenerative landscape processes.¹²

Lime (5%) Lime acts as a chemical stabiliser, reducing shrinkage during the curing phase and significantly increasing water resistance. By promoting pozzolanic reactions within the soil matrix, it enhances long-term strength and dimensional stability.⁹ This is particularly important in Barrare, where high porosity and seasonal saturation can degrade untreated materials.

Hemp (3%) Hemp fibres are incorporated for tensile reinforcement, helping to control micro-cracking during the drying phase and offering structural stability under thermal expansion.¹³ Its behaviour under strain mimics early fibre-reinforced systems and introduces a low-tech strategy for improving crack resistance, especially in large-scale printed or cast elements.

Fly Ash (10%)

Compost (5%)

Fly ash serves a dual role: mechanically, it increases density and fills voids in the mix, simulating fine particle distribution found in stabilised soils.¹⁰ Symbolically and contextually, it integrates a post-industrial byproduct reflective of the region’s coal-burning legacy. This allows us to reframe pollutant residues as active architectural ingredients, converting waste into performance.

Compost is added not merely as filler but as a biological activator. It supports the emergence of living surfaces by supplying nutrients and microbial life to the material body.¹⁴ This opens the door to a new typology of living architecture, one that supports mosses, lichens, or microbial coatings, particularly in shaded, high-humidity conditions typical of the Jharia region. By systematically varying these additives, we can begin to construct a material taxonomy where the mechanical, environmental, and ecological properties can be parametrically controlled. Each additive introduces a layer of responsiveness that can be tracked, simulated, and integrated into computational design workflows for adaptive architectural systems.¹⁵

1. Sherwood, P. T. Soil Stabilization with Cement and Lime. London: Transport Research Laboratory, 1993. 2. Siddique, Rafat. “Effect of Fly Ash on the Properties of Soil Stabilized with Lime.” Waste Management 24, no. 6 (2004): 583–589. https://doi.org/10.1016/j.wasman.2003.09.003 3. Lehmann, Johannes, and Stephen Joseph, eds. Biochar for Environmental Management: Science, Technology and Implementation. 2nd ed. London: Routledge, 2015. 4. Ziegler, C., and A. Battrick. “Living Architecture: Towards Sustainable, Responsive Building Skins.” Architectural Design 87, no. 2 (2017): 82–89. https://doi.org/10.1002/ad.2147 5. Balaguru, P. N., and S. P. Shah. Fiber-Reinforced Cement Composites. New York: McGraw-Hill, 1992. 6. Jones, Michael, et al. “Designing Living Architecture: Soil and Compost as Active Materials in Building Systems.” International Journal of Architectural Computing 18, no. 2 (2020): 133–150. https://doi.

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Fig. 4.14. Photo of Material Experiment Samples (author)

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Fig. 4.15. Robot adjusting to various ground conditions and approximate extent if the arm

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Fig. 4.14: Robot Dimensions

4.4.2 Material Tests and Calculations

Planned Material Testing & Evaluation A series of material tests are scheduled to evaluate the performance of bio-based and locally sourced composites for potential application in construction within degraded post-mining landscapes like Barrare. These tests are designed to assess structural integrity, environmental responsiveness, and digital fabrication compatibility. Compressive strength tests will be conducted at two curing intervals, 7 and 14 days, to track how the material gains strength over time, providing a baseline for its structural viability. Water absorption will be measured by recording the dry weight of cured samples, soaking them for 24 hours, and then measuring the post-soak weight. This will indicate porosity levels and the material’s resistance to water penetration, both critical factors in high humidity and monsoon regions. To understand the material’s interaction with its environment, pH testing will be carried out using two approaches: (1) soaking a cured sample in distilled water and testing the water’s pH after 24 hours to detect potential leaching, and (2) mixing a small amount of the material with water for a direct pH reading. These will help determine compatibility with plant growth and long-term soil contact. To assess printability, a syringe test will be conducted by extruding the fresh mix through a 4 mm diameter syringe, checking for flow consistency and shape retention, key indicators for 3D printing potential. Shear strength testing is also planned for fibre-reinforced variants using small-scale tensile rigs to explore elasticity, rupture behaviour, and bonding performance. Throughout the drying and curing phases, all samples will be monitored for deformation, shrinkage, and cracking to evaluate dimensional stability and risk under field conditions.

1. Vatanparast, S., Boschetto, A., Bottini, L., & Gaudenzi, P. (2023). New Trends in 4D Printing: A Critical Review. Applied Sciences, 13(13), 7744. 2. Lee, JaeMyung, and BooMee Park. A Study on Eco-Friendly Materials for 3D Printing – Focused on Korean Hwangto (Loess). Journal of Asian Architecture and Building Engineering, published online September 9, 2024. 3. Duan, Wenbo, Shunbo Zhao, Linbing Wang, Wensheng Wang, Shaopeng Wu, and Yiyu Lu. “Development of Low-Carbon Cementitious Composites with Soil and Industrial Solid Wastes for 3D Printing Construction.” Construction and Building Materials 398 (2024): 132820

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4.5 Material Experiments The aim from the outset wasn’t just to create a material that prints. It was to engineer a composite that could move through a robotic system, hold its shape after extrusion, resist cracking during drying, and support ecological growth over time. That meant tuning the mix not just for structural performance but also for bioreceptivity a dual challenge well-documented in recent research on earth-based additive manufacturing. Studies by Faleschini et al. (2023) highlight the benefits of combining locally sourced soils with lime and natural fibres to achieve low-carbon, shrinkage-resistant earthen composites. Ji et al. (2023) further stress the importance of managing clay content, particle gradation, and fibre reinforcement to finetune yield stress and thixotropy key parameters in printable earth systems.

1 Fiber reinforcement Akemah & Ben-Alon (2023) demonstrated how fibres like hemp and straw enhance tensile resistance while reducing shrinkage cracking. We adopted hemp at 3%, a ratio that bridges microcracks without clogging the auger, based on their findings and our extrusion observations.

2 Binder System We introduced 15% fly ash to densify the matrix and improve long-term durability, referencing studies on sustainable lime– fly ash formulations. Lime was set at 5%, following Bhusal et al. (2023), who found that moderate lime improves drying stability and pH balance — though too much increases brittleness.

3 Bioreceptivity Additive We added 3% biochar an unusual choice in structural printing but inspired by façade research where biochar improved moisture retention and microbial activity. This addition pushes the material toward our long-term goal: surfaces that are not just durable, but alive.

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Fig. 4.16. Material Sample (Photo by Author) 87

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Experiment 01 - Base Mix Soil | Organic matter | Clay | Hemp (46%)

(08%)

(15%)

(02%)

Mixture 01

Mixture 02

Mixture 03

Water (30%)

Water (40%)

Water (50%)

Experiment 02 – Strengthening Soil | Organic matter | Clay | Hemp | Water (32%)

(5.3%)

Mixture 04

(10.7%)

(1.6%)

(40%)

Mixture 05

Lime (5%) | Fly ash (8%)

Mixture 06

Lime (8%) | Fly ash (10%)

Lime (10%) | Fly ash (20%)

(5.3%)

(10.7%)

Mixture 07

(1.6%)

(4%)

Mixture 08

Biochar (10%)

Biochar (15%)

Fig. 4.17. Material Samples (author) 1. Akemah and Ben-Alon, Mechanical and Rheological Roles of Plant Fibres, 2023. 2. D’Alessandro, Ruello, and Ubertini, Rheology of Fibre-Reinforced Earthen Composites, 2021. 3. Perrot, Rangeard, and Pierre, Structural Built-Up of Cement-Based Materials, 2016. 4. Siddique, Utilization of Fly Ash in Construction, 2010.

(3%)

Mixture 09 Biochar (20%)

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4.5.2 Printibility

The base mix was derived from vernacular practices in Jharia, where mud walls are common, reinterpreted as a system suitable for robotic arm extrusion with local resources. Soil was sieved to ≤2 mm (80–85 wt.%) to ensure particle size uniformity and reduce clogging.² Cohesion was enhanced with 5–12 wt.% organic matter, while short straw fibres (2–3 wt.%) reduced drying shrinkage and improved tensile resistance.³ Water content (35–40 wt.%) was tuned to achieve shearthinning during extrusion and rapid build-up on deposition.⁶ Stabilisation was achieved with 5–10 wt.% lime and 10–20 wt.% fly ash, acting as fine pozzolanic fillers and regulating pH.⁴ Lime raised pH above 12 to promote pozzolanic activity and early buildability, but dosage was restricted to avoid brittleness reported in high-lime mixes (Bhusal et al., 2023).⁵⁶ Biochar (3–10 wt.%) was incorporated to enhance moisture retention, surface roughness, and microbial colonisation.⁷ Acting as micro-reservoirs, it reduced evaporation and maintained a wet microclimate favourable to moss and lichen growth, aligning with the project’s aim of creating a substrate that is both structural and environmentally active.⁸ Additive ratios strongly influenced performance. A small addition of 3 wt.% locust bean gum and 3 wt.% alginate improved stacking and green-phase stability of printed layers. Hemp fibre served as the main reinforcement, following Akemah and Ben-Alon (2023), who showed plant fibres improve ductility and bridge cracks in printed earth.¹ Hemp played a dual role: bridging shrinkage cracks and redistributing tensile stresses, while also forming a lattice that resisted plastic deformation.² Dosage was carefully limited to avoid clustering and nozzle clogging seen in high-fibre mixes.³ Overall, the formulation combined up to 85 wt.% local soil with biopolymers, fibres, and stabilisers to yield a print-ready substrate. The mix was dimensionally stable, adhesive between layers, and ecologically responsive, providing the basis for further mechanical and bio-receptivity testing.

Fig.4.18. Base-mix extrudability check of different samples 01, 02 ,03 respectively

Fig. 4.19. Base-mix extrudability check of different samples 09, 08 ,07 respectively

5. Walker and Stace, Properties of Cement Stabilised Earth Blocks, 1997. 6. Bhusal, Schmid, and Malinowski, Lime-Stabilised Earthen Materials, 2023. 7. Pacheco-Torgal et al., Eco-Efficient Construction and Building Materials, 2014. 8. Prieto, Hernández-Córdoba, and Lozano-García, Bio-Based Additive Manufacturing, 2020.

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Fig. 4.20. Extrusion tests

4.5.1 Setup

The printing system consisted of an airtight aluminium cylinder connected to a reinforced feed pipe, powered by 6 bar compressed air. Material flow was driven through a screw-driven auger extruder fitted with an 8 mm nozzle, with extrusion cycles controlled via an Arduino-based program. This allowed for precise control of feed rate, extrusion start/ stop, and strand length. The sealed cylinder prevented pressure loss, ensuring consistent delivery, while the auger’s progressive compression section maintained even material density before discharge. 90

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Experiment 04 – Final mix Base Mix | Fly ash + Lime | Biochar + Vermiculite | LBG + Alginate (32%)

(5.3%)

(10.7%)

Mixture 08 Hemp (15%)

(1.6%)

(5%)

Mixture 09 Hemp (20%)

Fig. 4.21. Inclined Surface Print test

4.5.3 Auger extrudability on terrain After laboratory-based tests of extrudability and printability, we continued with the biochar-rich base mixture selected based on deposition stability vs. reproducible extrusion performance in earlier tests. This iteration had the most uniform performance: adequate density to hold shape and a moisture retention profile that was compatible with our bioreceptivity goals. To simulate site application, the mixture was extruded directly onto prepared ground surfaces at 30° and 60° slopes. On the 30° slope, the filament showed both geometry and adhesion with no distortion, indicating that the mix’s surface tack and internal cohesion were appropriately balanced to moderate slopes. On 60°, gravity effects started to be apparent: strands showed controlled settling but in the absence of detachment, which suggests that slope stability was more limited by viscosity and shear recovery time than by intrinsic cohesion. It is worth mentioning that in comparison with the previous lime–fly ash–fibre iteration, this biochar mixture gave lower extrusion rates. This resulted from the modestly higher plastic viscosity contributed by the biochars’ fine porous structure, increasing water requirement and generating greater resistance in the auger feed. Despite this delayed deposition, it also had the effect of improved buildability on gradient planes by reducing immediate slump upon release of shear, an effect consistent with specific viscosity adjustment in highperformance extrusion processes. These ground tests confirmed that the formulation as currently attained can be applied under normal slope levels without support and that further optimization of rheology namely reducing viscosity but retaining structural yield will improve its applicability to steeper inclines. 91

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Fig. 4.22. Mockup to test material Behavior at various inclinations of the base

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4.5.4 Auger extrudability on non-planar surface

This trial extended our terrain-printing tests to a steeper angled surface, using the biochar-enriched base mix without any commercial 3D printing additives. The intention was to evaluate how the natural material composition alone performed under high-gradient deposition, and to test its potential for forming a bioreceptive structure with intentional porosity. The printed lattice was designed to encourage surface roughness and open voids, providing multiple anchoring points for moss, lichen, and other pioneer species. On the steep incline, however, the absence of viscosity-modifying agents meant that the material’s shear recovery rate was not fast enough to prevent localised slump and deformation in certain regions. This experiment highlighted the critical role of rheology control; without it, even structurally stable mixes can lose form on challenging terrain. Despite these limitations, the trial successfully demonstrated the feasibility of printing open-structured, porous geometries directly on angled substrates. Moving forward, the focus will be on refining the mix with targeted rheology adjustments, potentially using natural binders or bio-compatible thickeners, to achieve greater buildability while preserving the ecological and low-carbon characteristics of the material

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Fig. 4.23. Compression test results

4.5.4 Compression Test Compression tests on samples 6–11 were to test the impact of density, mass, and composition variation on the mechanical performance of bio-receptive earthen composites. Samples had identical raw soil foundation, stabilisers (lime, fly ash), and bio-receptive additives (biochar, hemp fibres, organic matter) but different proportions and water content for varying densities. The results are the compromises between structural capacity and ecological responsiveness in the system. The densest sample, Sample 8 (1.170 g/cm³), had an intermediate amount of strength with a failure load of 800 kg. Its density allowed for the inclusion of restricted pore space, which reduced microcracking under load but perhaps not the highest strength. The opposite was observed for the sparsest sample, Sample 10 (0.655 g/cm³), which recorded one of the highest ultimate loads at 1100 kg. This suggests that voids and fibre presence, even reducing density, better redistributed stress and delayed catastrophic failure. The outcome highlights the non-linear relationship between density and strength in the example of fibrous soil composites. The optimal overall performance was found in Sample 11, which involved a mid-density of 0.773 g/cm³ with a maximum

load capacity of 1100 kg. Its balance of porosity and stiffness appears optimal to support load with the bio-receptiveness essential for long-term ecological growth. Conversely, certain samples such as Sample 7 (1.112 g/cm³) collapsed earlier under respective loads, confirming that higher compaction is not always synonymous with higher strength in mixtures incorporating organic fibres and biochar. These results show that composite strength in earthen materials is not solely a function of density. Particle packing, fibre reinforcement, and internal porosity all contribute to a range of performance. The findings also show that structurally reliability optimised material, like Sample 11, may also be water absorbent, pH moderating, and biologically growfriendly without ever being unsafe under compression. In the scenario of the Jharia site, where ecological recovery and ground instability are the most critical issues, such behaviour is particularly significant. It demonstrates that materials can be tuned not to reach a maximum density but to a balanced ratio of strength, resilience, and bio-receptivity, enabling construction on degraded ground while repairing it simultaneously.

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Sample 6

Sample 7

Sample 8

Max Load: 750 kg

Max Load: 800 kg

Sample 9

Sample 10

Sample 11

Max Load: 1100 kg

Max Load: 1100 kg (highest strength)

Mass: 455 g Height: 7 cm Density: 1.022 g/cm³

Mass: 304 g Height: 6 cm Density: 0.796 g/cm³ Max Load: 900 kg

Mass: 495 g Height: 7 cm Density: 1.112 g/cm³

Mass: 250 g Height: 6 cm Density: 0.655 g/cm³ (lowest density)

Mass: 521 g Height: 7 cm Density: 1.170 g/cm³ (highest density overall)

Mass: 295 g Height: 6 cm Density: 0.773 g/cm³

Fig. 4.24. Compression test results 95

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Fig. 4.25. Shear test results

4.5.5 Shear Test The shear test was carried out to evaluate the structural behaviour of the bio-receptive soil composite under lateral stress. On loading, the specimen resisted displacement with no visible cracking, an indication of excellent cohesion between the soil matrix and the fibrous additives. With the increase in applied load, minimal cracks in the shape of micro-fractures began to develop along the potential shear plane, which continually grew until catastrophizing slippage was achieved. This transition was the highest load bearing capacity of the sample, which was succeeded by a post-transition brittle failure. Observation confirmed that the test specimen had a relatively clean shear break rather than crushing or delamination, which means the mixture had formed a stable internal bond with compressive confinement but lost resistance subsequent to mobilization of the shear plane. The presence of some fibre pull-out showed organic matter contributed to micro-crack

bridging but not sufficiently to halt progress in failure. On an operational basis, what this result implies is that the composite is shear-stress resistant to medium levels similar to those of low-strength masonry mortars and consequently structurally stable for non-load-bearing printed walls or infill structures. More importantly, sequential failure observed from video analysis helps in calibrating the material model: strengths derived from the test serve as input parameters for robotic printing on slopes or on uneven ground. Generally, the shear test confirmed that the mix is still intact up to a critical point, beyond which brittle sliding will dominate. This point, in terms of shear strength in MPa, provides the numerical value needed for comparison of the material with conventional masonry units and the optimization of additive ratios in subsequent tests.

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Sample 10

Mass: 295 g Height: 9 cm Density: 0.685 g/cm³ (lowest density) Max Load: 900–1200 kg Shear Strength: 3.2–4.2 MPa Failure Mode: Clear central shear plane

Sample 11

Mass: 298 g Height: 9 cm Density: 0.773 g/cm³ (highest strength) Max Shear Load: 950–1300 kg Shear Strength: 3.4–4.6 MPa Failure Mode: Diagonal split with shear

Fig. 4.26. Shear test results 97

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4.5.6 pH Test

Fig. 4.26. pH test results

To assess the material composite’s chemical stability and acid resistance, four selected samples were subjected to pH testing, which included Sample 8, Sample 9, Sample 10, and Sample 11. The test was conducted to simulate environmental conditions of acid exposure such as acid rain, microbial metabolites, or root exudates, which are the most critical conditions for assessing a material’s potential bio-receptivity. The ability of a surface to offer a stable pH environment is significant, as microbial colonization and biological activity are highly sensitive to variations in pH.

in the form of alginate and locust bean gum, and biochar and vermiculite ingredients that have been shown to enhance both pH stabilization and microbial compatibility. Sample 9 with no buffer agents and high fly ash concentration but minimal organic matter yielded the worst buffering capability, with pH values remaining in the acidic range of 5.0 to 5.8. Sample 10 was an improvement on Sample 9, perhaps due to the presence of biochar and vermiculite, with a mid-range pH of 5.8 to 6.5. Sample 11 followed on from this, with end pHs between 6.2 and 6.8. Although it didn’t achieve the full neutrality of Sample 8, it got a good balance between material stability and buffering capacity.

Approximately 20% vinegar, with the calculated pH of 3.8, was applied to each dry sample to provide controlled acidity. Samples were incubated with the solution for 72 hours at room temperature. After incubation, the pH of the remaining water was measured using a standard analog soil pH meter. Although not highly precise, this device is adequate to identify overall trends and compare buffering capacity between the materials. The results indicated a clear buffering gradient. Sample 8 returned the most significant buffering activity, returning to a very close-neutral pH range of approximately 6.8 to 7.0. This was because it contained high levels of organic matter

These results indicate that the mixtures’ acid-buffering capacity is directly influenced by the makeup of the mixtures. The combination of natural polysaccharides, biochar, and mineral admixtures such as vermiculite stabilized the pH of the samples. This is particularly significant for materials that will support biological organisms or improve ecological integration, in which a stable and benign chemical condition is necessary. Among the tested materials, Sample 8 was chemically bio-most compatible, and Sample 11 had an even performance with structural strength and an optimal microenvironment for the growth of living things

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4.5.7 Water Retention Test

Fig. 4.27. Water retention test results

The 24 h water retention values of Samples 6–11 demonstrate how some compositions of additives and stabilisers regulate porosity and water behaviour directly. Sample 8 recorded the maximum value (~65 g) due to the synergistic action of 10% biochar and 5% vermiculite: the microporosity of biochar trapped capillary water and vermiculite swelled to hold interlayer water, creating an extremely absorptive matrix.

Optimum performance is obtained from a balance of these, such that composites maximize strength and durability or bioreceptivity and ecological activation, depending on design intent.

In contrast, Sample 9, containing 15% lime and fly ash as primary stabilisers, absorbed only ~12 g. The pozzolanic reaction closed void networks, forming a compact matrix with minimal pore connectivity. Samples 6 and 7, containing intermediate hemp fibre (2–3%) and lower amounts of lime/ fly ash, maintained ~25–35 g, suggesting that fibres improved water distribution but were too dense for long-term storage. Samples 10 and 11, balanced mixtures with both mineral binders and trace organic additives, stabilized at ~15–20 g, a compromise between structure stability and environmental sensitivity. The test confirms that bio-additives (vermiculite, hemp, biochar) modify porosity and retention, while mineral binders (fly ash, lime) increase density and prevent absorption.

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Fig. 4.27. Material Samples

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4.5.8 Optimal Material Mix Comparative testing of Mixtures 6–11 showed how each additive reshapes the behavior of mud in measurable but also tactical ways. The most lime- and fly ash-dense Mixtures (Samples 8 and 10) cured quickly and showed the highest values of strength and stability. The pozzolanic reactions chemically bound the particles of the soil into each other, resulting in dense, stonelike composite materials that resist compression and deformation. However, the same density reduced porosity and led to weak water retention and ecological responsiveness failure. Conversely, mixtures with increased biochar and compost ratio (Samples 6 and 7) performed exceedingly well in water retention and pH neutralizing. Porous carbon structure of biochar and organic contents of compost created pathways for moisture, microbial activity, and chemical balance but weakened the load-bearing capacity of the material. Fig. 4.28. Comparative Analysis

The optimum was attained with Mixture 11, which had stabilisers and green colourants in balance. It did not achieve a peak in any single aspect but possessed medium-to-high values across the board. The mixture was strong enough to retain its shape, porous enough to take in water, inert enough to sustain plant growth, and stable enough to survive curing without severe distortion. In the hostile environment of Jharia, where ground is unstable and environments are toxic, such equilibrium is more valuable than personal optimisation. The most significant lesson from such tests is that additives are not inert fillers but active design tools. They shift the mud behaviour towards either structural safety or eco-recovery, and real opportunity lies in mediating a marriage between the two. What I found is that material can no longer be thought of as an intransigent limit—it can be coded, calibrated, and is dependent upon context. The implication is not a one-mixturefor-all locus, but that Mixture 11 demonstrates the possibility of an adaptive equilibrium: a hybrid that is structurally resilient, ecologically sensitive, and can be installed by robotic fabrication in unstable ground. This is why the tests were important—utilizing mud as an old medium, they turned it into a strategic agent for landscape and architecture remediation.

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Global warming t CO2e - Life-cycle stages Global warming t CO2e - Life-cycle stages

All raw material extraction + processing -52.6 % Fly ash+ lime stabiliser - 37.2% Trucking from suppliers - 4.4% Hemp straw - 5.8% On-site processes - 0.0%

Global warming t CO2e - Resources types Global warming t CO2e - Life-cycle stages

Raw soil - 48.2% Fly ash - 37.5% Biochar - 7.7% Hemp Straw - 6.3 Lime stabilizer - 0.3%vv

Fig. 4.29. LCA Analysis Charts

4.5.9 Life Cycle Assessment (LCA) The Jharia earthen–bio-based system shows that soil, agricultural fibres, and industrial by-products can replace conventional brick and cement masonry with strong carbon performance. Life cycle assessment reports cradle-to-gate emissions of 27 kilograms of CO₂e per square metre of wall (A1–A3), placing it in the highest global category. Its footprint is about ten times lower than typical concrete or masonry, proving the potential for major reductions in environmental impact. The analysis highlights strengths and areas for improvement. Soil currently dominates the footprint because a conservative dataset uses energy-intensive dry sand as a proxy. In practice, raw excavated soil would yield much lower emissions, meaning reported results likely overestimate impact and the system’s true performance is even more favourable. Hemp fibres account for just over twelve percent of emissions from processing and transport, but this excludes the carbon stored in the fibres, which under long-term rules could offset much of it. Biochar shows a similar pattern: while its direct footprint is small, its real value lies in long-term carbon storage. Both materials suggest a future where bio-based inputs are functional and central to net-negative construction. Binders remain the main hotspot, even with clinker reduced

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by half through fly ash and lime, confirming that cement chemistry is the biggest barrier to lowering embodied carbon. Alternatives such as calcined clays, expanded SCMs, or geopolymeric binders represent the next frontier. From a life-cycle view, about ninety-five percent of emissions occur in sourcing and processing, with transport adding only four percent thanks to regional supply, while on-site emissions are negligible. This shows that material choice and production methods are far more decisive than logistics or assembly. The study reveals not only efficiency but also potential. Even with conservative modelling, the system performs at the highest global level. If the lower-impact potential of raw earth and the carbon storage of hemp and biochar are credited, the Jharia mix could move from low-carbon to carbon-negative. This marks a shift toward construction that restores rather than depletes. Together, the findings show that structural materials can be both robust and restorative. By using local soil, agricultural by-products, and industrial residues, Jharia offers a model for redefining construction in similar contexts. The approach cuts embodied carbon to a fraction of conventional levels while supporting regional circular economies, proving that the next generation of materials can be locally sourced, made from available resources, and still surpass global benchmarks.

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Fig. 4.30. Mock up image

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4.5 The Structure : Form-finding 4.5.1 Material and Planting Interplay

Identified zones in the stabilization network system are intervened with a bio-receptive structure for multifunctional reasons. The structure serves the purpose of helping stabilizing the unstable grounds in combination with plants that also facilitate the same, hence its bio-receptive nature. In addition to that it also acts as foundation for architectural intervention that would be anchored to the structure after years of remediation when the site is suitable for habitation. The corridor provides information of slope, extent of intervention, subsidence, soil type and allocated planting. These characteristic form the basis for the design of the proposed bio-receptive structure. In the initial stages the site would be intervened after minimal land dressing and preparation for material deposition process. Once the structures are constructed allocated planting takes over and together it facilitates stabilization. After about 10 to 15 years the structure acts as foundations to architecture. The diagram represents the conceptual stages of the adaptation of the structure. Going forward three methods of the formation of the morphology are explored and the suitable interplay of plant and material is carried forward. Experiments are conducted for different plant pocket formations where light organic soil is filled in the voids that would help in plant growth. The scale, location and depth of these pockets in relation to material and planting have various possibilities that in turn affect the structural behavior. A 5x5 m patch is selected for testing different porosotires that can be achieved for planting integration.

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Terrain with varying ground conditions

Stabilizing Ground Bioreceptive material with planting pockets

The material and plants stabilize and remediate ground

Architectural extension

Bioreceptive structure act as foundation for anchor

Fig.4.31. Conceptual diagram of the time-steps of the bioreceptive structure 105

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Planting Variations as per micro ground conditions

4.5.2 Iteration 1 Material and Planting

This iteration explores the possibility of planting distribution based on micro ground conditions, small, medium and large plants are dispersed on a chosen demonstration patch of the site. To explore the different densities of plants and material which vary based on plant type- the reaction diffusion algorithm based on the theory of Gary Scott is employed. The algorithm represents the interaction of two chemicals that merger to form numerous patterns and variables such as rate of dispersion of both materials, feed rates of second chemical and death rates of the first chemical. This is abstracted to apply on planting and material formations. As planting needs to interact with material different plant types would form different patterns. Here the experiment is about changing these variables to form a catalogue for evaluation in different condition. The figure indicates the variables the the generated outcomes. They are broadly categorized for suitability for small, medium and large plants. The figure demonstrates few of the many emergent outcomes. These parameters are applied on specific planting conditions.

Displacement Analysis

Discussion Although this form-finding method integrates planting variations, the gap formed between different pattern creates structural discontinuity. In addition to that the performative thickness is difficult to vary seamlessly across the structure. The direction of material pockets hinders the necessary force flow. This indicates that structural performance needs to be prioritised and planting pockets can vary based on structural and material need.

Section Fig.4.32. Iteration 1 Developement

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0cm

50cm

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Larger plants in middle - Planting Allocation

Material and Planting

4.5.3 Iteration 2

This iteration explores the distribution of chosen plants which are then clubbed in groups of four to five to form a combined pocket. The thickness of the structure varies such that more thickness is provided for bigger plants that need bigger planting pocket and soil depth. A secondary layer of material is created at the bottom that would ensure force flow and this double layered system helps in growing plants intertwined over the bottom layer and overtime this anchors the structure to the soil. This is created using a voronoi cells of varying sizes based on the force flow required in the retaining structure.

Displacement Analysis

0cm

5cm

Discussion This creates planting pockets that provide necessary diameter for plants to grow however these large pockets instead of retaining earth could cause sliding of the plants on steep slopes as the soil or plants are not retained in the direction of the slope. The bottom layer however offers variation in densities based on plant sizes which can be explored further.

Section Fig.4.33. Iteration 2 Developement 107

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Varying pockets based on stress lines simulation

Material and Planting

4.5.4 Iteration 3

This iteration uses a generative method: stress lines in the base shell guide the subtraction of material from redundant areas, while continuity is increased where force flow is required. A Voronoi cell strategy, driven by the stress-line simulation, distributes planting pockets across the geometry. Some larger cells are subdivided to create a bottom layer. Large pockets accommodate larger plants, and smaller pockets suit smaller species.

Displacement Analysis

Discussion Although each pocket can hold a plant and material contact for each plant would help in material aiding the plant growth, however this technique doesn’t necessarily offer enough layers at the bottom and the plants mostly contact the ground. Although the displacement analysis shows necessary structural performance. This methods prevails including large species.

Section Fig.4.34. Iteration 3 Developement

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Discussion The three iterations tested become the grounds for further design development. As learnt from the first experiment that while varying planting pockets could help in creating better interface of plants and material the overall structural performance for loads that can be exerted vertically and also from the retention pockets. Supports play a crucial role in generating a force flow which help in decisions for material subtraction. This generative method is explored further in the design development. Planting and their growth is kept in mind however considering the slope of the terrain it is not ideal to provide large circular pockets as this could lead to sliding of the organic matter and instead a geometry more in the direction of iteration 3 would help in retention.

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Construction and Building Materials 404 (2023): 133114. 16. Faleschini, Flora, et al. “Sustainable Mixes for 3D Printing of Earth-Based Constructions.” Construction and Building Materials 398 (2023): 132496. 17. Deshpande, Ananya. Burning Ground: Infrastructure and Survival in Jharia’s Coalfields. New Delhi: Earthline Press, 2021.

Chea, C. P., Bai, Y., Pan, X., & Arashpour, M. (2020). An Integrated Review of Automation and Robotic Technologies for Structural Prefabrication and Construction. Journal of Engineering Safety and Environment, 2(2), 81–98.

Ardiny, H. (2017). Functional and Adaptive Construction for Rescue: An Analysis of the Approach Using Autonomous Robots. EPFL.

19. Ardiny, H. Functional and Adaptive Construction for Rescue: An Analysis of the Approach Using Autonomous Robots. Lausanne: EPFL, 2017.

Vatanparast, S., Boschetto, A., Bottini, L., & Gaudenzi, P. (2023). New Trends in 4D Printing: A Critical Review. Applied Sciences, 13(13), 7744.

20. Maiti, S. K. Ecorestoration of the Coalmine-Degraded Lands: Indian Scenario. New Delhi: Springer, 2021.

18. Chea, C. P., Y. Bai, X. Pan, and M. Arashpour. “An Integrated Review of Automation and Robotic Technologies for Structural Prefabrication and Construction.” Journal of Engineering Safety and Environment 2, no. 2 (2020): 81–98.

Lee, JaeMyung, and BooMee Park. A Study on Eco-Friendly Materials for 3D Printing – Focused on Korean Hwangto (Loess). Journal of Asian Architecture and Building Engineering, published online September 9, 2024.

21. Singh, G., and B. K. Tewary. “Impact of Coal Mining and Mine Fires on the Local Environment in Jharia.” Environmental Monitoring and Assessment 186, no. 10 (2014): 5955–5964. https://doi.org/10.1007/s10661-0143824-7.

Duan, Wenbo, Shunbo Zhao, Linbing Wang, Wensheng Wang, Shaopeng Wu, and Yiyu Lu. “Development of LowCarbon Cementitious Composites with Soil and Industrial Solid Wastes aMaterials 398 (2024): 132820

22. Sarkar, D., and R. Rano. “Soil Physical Constraints in Degraded Landscapes of Eastern India.” In Soil Degradation and Restoration in India, edited by A. Bandyopadhyay et al., 131–148. New Delhi: Springer, 2020.

Indian School of Mines. Degradation of Soil Quality Parameters. Dhanbad: Indian School of Mining,

23. Based on site soil testing conducted by the research team in Barrare, Jharkhand, 2024.

Central Institute of Mining and Fuel Research. Soil Characteristics in Dhanbad–Jharia Township Area. Dhanbad: CIMFR

24. Bhattacharya, P., and N. Chakraborty. “Soil Compaction and Water Stress in Coal Mining Regions of Jharkhand.” Indian Journal of Soil Conservation 45, no. 2 (2017): 112– 120.

10. Mandal, Mukesh Kumar, Vinay Bachkaiya, Alok Tiwari, Shivani Yadav, and Siddharth Shankar Patre. “Effect of Fly Ash, Lime and Vermicompost Application on PhysicoChemical Properties of Soil.” The Pharma Innovation Journal 10, no. 8 (2021): 83–87. 11. Amiralian, Saeid, Amin Chegenizadeh, and Hamid Nikraz. “A Review on the Lime and Fly Ash Application in Soil Stabilization.” International Journal of Biological, Ecological and Environmental Sciences 1, no. 3 (2012): 124–126. 12. Faleschini, Flora, et al. “Sustainable Mixes for 3D Printing of Earth-Based Constructions.” Construction and Building Materials 398 (2023): 132496. https://doi.org/10.1016/j. conbuildmat.2023.132496. 13. Ji, Yameng, Philippe Poullain, and Nordine Leklou. “The Selection and Design of Earthen Materials for 3D Printing.” Construction and Building Materials 404 (2023): 133114. https://doi.org/10.1016/j.conbuildmat.2023.133114. 14. Akemah, Tashania, and Lola Ben-Alon. “Developing 3D-Printed Natural Fiber-Based Mixtures.” In ICBBM 2023: Bio-Based Building Materials, edited by Sofiane Amziane et al., 555–572. Cham: Springer, 2023. https://doi. org/10.1007/978-3-031-33465-8_42. 15. Ji, Yameng, Philippe Poullain, and Nordine Leklou. “The Selection and Design of Earthen Materials for 3D Printing.” 110

25. Sherwood, P. T. Soil Stabilization with Cement and Lime. London: Transport Research Laboratory, 1993. 26. Siddique, Rafat. “Effect of Fly Ash on the Properties of Soil Stabilized with Lime.” Waste Management 24, no. 6 (2004): 583–589. https://doi.org/10.1016/j.wasman.2003.09.003. 27. Lehmann, Johannes, and Stephen Joseph, eds. Biochar for Environmental Management: Science, Technology and Implementation. 2nd ed. London: Routledge, 2015. 28. Ziegler, C., and A. Battrick. “Living Architecture: Towards Sustainable, Responsive Building Skins.” Architectural Design 87, no. 2 (2017): 82–89. https://doi.org/10.1002/ ad.2147. 29. Balaguru, P. N., and S. P. Shah. Fiber-Reinforced Cement Composites. New York: McGraw-Hill, 1992. 30. Jones, Michael, et al. “Designing Living Architecture: Soil and Compost as Active Materials in Building Systems.” International Journal of Architectural Computing 18, no. 2 (2020): 133–150. https://doi. org/10.1177/1478077120926659. 31. Oxman, Neri. “Material Ecology.” Journal of Design and Science 1 (2016). https://doi.org/10.21428/7e0583ad. 32. Vatanparast, S., A. Boschetto, L. Bottini, and P. Gaudenzi. “New Trends in 4D Printing: A Critical Review.” Applied

Research Development

Sciences 13, no. 13 (2023): 7744. 33. Lee, JaeMyung, and BooMee Park. “A Study on EcoFriendly Materials for 3D Printing – Focused on Korean Hwangto (Loess).” Journal of Asian Architecture and Building Engineering, published online September 9, 2024. 34. Duan, Wenbo, Shunbo Zhao, Linbing Wang, Wensheng Wang, Shaopeng Wu, and Yiyu Lu. “Development of LowCarbon Cementitious Composites with Soil and Industrial Solid Wastes for 3D Printing Construction.” Construction and Building Materials 398 (2024): 132820. 35. Akemah, Emmanuel, and Leeor Ben-Alon. “Mechanical and Rheological Roles of Plant Fibres in 3D Printed Earthen Materials.” Journal of Building Engineering 72 (2023): 106520. 36. D’Alessandro, Antonio, Marco L. Ruello, and Filippo Ubertini. “Rheology of Fibre-Reinforced Earthen Composites for Extrusion-Based Additive Manufacturing.” Construction and Building Materials 291 (2021): 123264. 37. Perrot, Alain, Didier Rangeard, and Alban Pierre. “Structural Built-Up of Cement-Based Materials Used for 3D-Printing Extrusion Techniques.” Materials and Structures 49 (2016): 1213–1220. 38. Siddique, Rafat. “Utilization of Fly Ash in Construction: A Review.” Resources, Conservation and Recycling 54, no. 12 (2010): 1043–1051. 39. Walker, Peter, and Tony Stace. “Properties of Some Cement Stabilised Compressed Earth Blocks and Mortars.” Materials and Structures 30 (1997): 545–551. 40. Bhusal, Sushil, Markus Schmid, and Mateusz Malinowski. “Lime-Stabilised Earthen Materials: Brittleness and Mechanical Performance in Extrusion Contexts.” Construction and Building Materials 365 (2023): 130167. 41. Pacheco-Torgal, Fernando, Said Jalali, António F. Marques, and Jalal Barros. Eco-Efficient Construction and Building Materials: Bioreceptive Materials and Biochar Applications. Cambridge: Woodhead Publishing, 2014. 42. Prieto, Ana, Rubén Hernández-Córdoba, and José Antonio Lozano-García. “Bio-Based Additive Manufacturing: Prospects for Microbial Colonisation and Ecological Performance.” Journal of Cleaner Production 258 (2020): 120578. 43. Barrare, Jharkhand, 2024. 44. Bhattacharya, P., and N. Chakraborty. “Soil Compaction and Water Stress in Coal Mining Regions of Jharkhand.” Indian Journal of Soil Conservation 45, no. 2 (2017): 112– 120. 45. Sherwood, P. T. Soil Stabilization with Cement and Lime. London: Transport Research Laboratory, 1993. 46. Siddique, Rafat. “Effect of Fly Ash on the Properties of Soil Stabilized with Lime.” Waste Management 24, no. 6 (2004): 583–589. https://doi.org/10.1016/j.wasman.2003.09.003. 47. Lehmann, Johannes, and Stephen Joseph, eds. Biochar for Environmental Management: Science, Technology and Implementation. 2nd ed. London: Routledge, 2015.

48. Ziegler, C., and A. Battrick. “Living Architecture: Towards Sustainable, Responsive Building Skins.” Architectural Design 87, no. 2 (2017): 82–89. https://doi.org/10.1002/ ad.2147. 49. Balaguru, P. N., and S. P. Shah. Fiber-Reinforced Cement Composites. New York: McGraw-Hill, 1992. 50. Jones, Michael, et al. “Designing Living Architecture: Soil and Compost as Active Materials in Building Systems.” International Journal of Architectural Computing 18, no. 2 (2020): 133–150. https://doi. org/10.1177/1478077120926659. 51. Oxman, Neri. “Material Ecology.” Journal of Design and Science 1 (2016). https://doi.org/10.21428/7e0583ad. 52. Vatanparast, S., A. Boschetto, L. Bottini, and P. Gaudenzi. “New Trends in 4D Printing: A Critical Review.” Applied Sciences 13, no. 13 (2023): 7744. 53. Lee, JaeMyung, and BooMee Park. “A Study on EcoFriendly Materials for 3D Printing – Focused on Korean Hwangto (Loess).” Journal of Asian Architecture and Building Engineering, published online September 9, 2024. 54. Duan, Wenbo, Shunbo Zhao, Linbing Wang, Wensheng Wang, Shaopeng Wu, and Yiyu Lu. “Development of LowCarbon Cementitious Composites with Soil and Industrial Solid Wastes for 3D Printing Construction.” Construction and Building Materials 398 (2024): 132820. 55. Akemah, Emmanuel, and Leeor Ben-Alon. “Mechanical and Rheological Roles of Plant Fibres in 3D Printed Earthen Materials.” Journal of Building Engineering 72 (2023): 106520. 56. D’Alessandro, Antonio, Marco L. Ruello, and Filippo Ubertini. “Rheology of Fibre-Reinforced Earthen Composites for Extrusion-Based Additive Manufacturing.” Construction and Building Materials 291 (2021): 123264. 57. Perrot, Alain, Didier Rangeard, and Alban Pierre. “Structural Built-Up of Cement-Based Materials Used for 3D-Printing Extrusion Techniques.” Materials and Structures 49 (2016): 1213–1220. 58. Siddique, Rafat. “Utilization of Fly Ash in Construction: A Review.” Resources, Conservation and Recycling 54, no. 12 (2010): 1043–1051. 59. Walker, Peter, and Tony Stace. “Properties of Some Cement Stabilised Compressed Earth Blocks and Mortars.” Materials and Structures 30 (1997): 545–551. 60. Bhusal, Sushil, Markus Schmid, and Mateusz Malinowski. “Lime-Stabilised Earthen Materials: Brittleness and Mechanical Performance in Extrusion Contexts.” Construction and Building Materials 365 (2023): 130167. 61. Pacheco-Torgal, Fernando, Said Jalali, António F. Marques, and Jalal Barros. Eco-Efficient Construction and Building Materials: Bioreceptive Materials and Biochar Applications. Cambridge: Woodhead Publishing, 2014. 62. Prieto, Ana, Rubén Hernández-Córdoba, and José Antonio Lozano-García. “Bio-Based Additive Manufacturing: Prospects for Microbial Colonisation and Ecological Performance.” Journal of Cleaner Production 258 (2020): 120578. 111

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Fig. 5.0 : Data Driven interventions at macro to material scale

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DESIGN DEVELOPMENT

The ‘Research Development’ chapter analysed terrain phenomena and decoded its potential, while this ‘Design Development’ chapter synthesises those results to generate and optimise tangible spatial and material solutions. It constitutes a process that actively explores and advances design using data as inputs, forming the core of the project’s computational design workflow. The chapter documents how the design is progressively specified along four axes: system, settlement, material, and structure. First, the System part details the implementation of the complex behavioural logic of agent-based modelling and traces the full procedure by which a multi-objective optimisation algorithm evaluates thousands of alternatives to derive an optimal stabilisation-corridor master plan. Second, the Settlement part applies algorithmic clustering to this master plan and arranges a concrete spatial programme for productive settlement. Third, the Material part verifies material performance through physical prototyping and establishes robotic 3D-printing fabrication strategies for constructing on irregular, sloping ground. Finally, the Structure part completes a living eco-structural system by means of C# logic that integrates structural modelling, soil–structure interaction, and ecological porosity. Taken together, these stages demonstrate that the final design proposal is not a merely formal outcome but the logical consequence of a rigorous, multi-scalar problem-solving process.

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5.1 The System : Searching 5.1.1 Agent-Based Network Exploration

Agent-Based Modelling was conducted to generate the stabilisation corridor, utilising the intelligent environment established in the research development chapter. The core of the methodology is to convert the multilayered data decoded in 4.1 into a dynamic force field that directly guides and constrains agent behaviour. Each 5 × 5 m patch carries unique data values, forming a virtual energy landscape that influences agents’ moment-to-moment decisions. Within this field, agent movement is governed by the composite interaction of four core behavioural algorithms. Rather than merely seeking the shortest path, these algorithms generate thousands of potential network alternatives that collectively address physical stability, social connectivity and structural robustness, thereby producing a rich gene pool of solutions for subsequent optimisation.

Behavioural Rule-Setting: Agents’ every movement is determined by the two most basic forces, namely Attraction and Repulsion. Attraction : Attractors are target locations that agents must reach or pass through, reflecting the core objectives of the project. In this simulation, to satisfy multilayered goals, two categories of attractor were specified. First, High Slope and High Subsidence data act as strong attractors directly tied to the primary engineering aim of resolving instability. Agents are continually drawn towards areas with elevated values, thereby forming routes that first connect and stabilise the most hazardous zones. Second, the Network to Village attractor reflects the goal of social sustainability, ensuring that the restored land is organically connected to surrounding communities; coordinates of existing villages adjacent to the abandoned mines were registered as additional attractors so that the emergent network functions as usable social infrastructure. Repulsion : By contrasts, repulsors denote obstacles or constraints that agents should avoid. Rock Formation zones act as strong repulsors because of excavation difficulty and high construction cost, encouraging agents to bypass these areas. Embedding this constraint from the outset ensures that the simulation outputs internalise buildability and economic viability.

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Fig. 5.1. Agent Movement on terrain

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Fig. 5.2. Data Layers 116

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Fig.5.3. Attractors and Repulsion points. 117

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To organise each agents’ movement into intelligent collective action, three distinct movement behaviour algorithms were operating as one complex system. Stigmergy: Agents leave a virtual trace, like a ‘pheromone’, on the paths they travel. Other agents are more likely to follow paths with stronger traces. By repeating this process, the most efficient routes are collectively reinforced, much like ants finding the shortest path. Flocking: Agents do not move individually but in groups, maintaining a certain distance from and aligning their direction with nearby agents. This increases the efficiency of exploration and prevents the network from becoming overly fragmented. Weaving Wandering : Weaving Wandering is an algorithm that balances exploration and network density. It facilitates broad surveying and efficient path discovery while ensuring a robust, integrated network by guiding trajectories to intersect. This allows for free exploratory search that consolidates into a stable, mesh-like network.

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Fig.5.4. Agent Movement Simulation.

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5.1.2 Multi-Objective Optimisation

The thousands of potential corridor alternatives generated through the Intelligent Environment and Agent-Based Modelling (ABM) reveal a complex balance between multiple conflicting objectives. This study aims to systematically classify and evaluate this vast solution space using Multi-Objective Optimisation(MOO) and unsupervised learning. The goal is to transparently select the solution that best aligns with the project’s core objectives in a data-driven manner. (1) Optimal Solution Search through Evolutionary Algorithm : Optimisation Setup and Objective Function Definition (All to be Minimised) The simulation was configured to explore 800 unique solutions in total, with a Generation Size of 20 and a Generation Count of 40. To control genetic diversity and convergence speed, the Crossover Probability was set to 0.9 and the Mutation Probability to 1/n (where n = number of genes). The optimisation goal is to Minimise the values of the following six fitness functions. Terrain stability, terrace area, and travel distance are examples of objectives where “maximisation” is desired. To ensure consistency, these objectives were transformed into minimisation problems by taking their reciprocal (1/value), where a smaller value denotes better performance.

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Maximise Terrain Stability: How many (attractors) the corridor passes through.

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Maximise Terrace Area: The area of flat land available for agriculture or construction after the corridor is created. Minimise Corridor Slope: A gentle slope for ease of pedestrian access and maintenance. Minimise Total Length: The shortest path to reduce material use and construction costs. Minimum and Maximum Travel Distance: To prevent agents from stagnating in one area, they were required to travel at least 40m.

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Fig.5.5. Parallel Coordinate Plot of the Pareto optimal solution set resulting from the optimisation

Analysis of the Evolution Process (Interpretation by Graph) Standard Deviation / KDE Graph: This graph illustrates the performance distribution of the solution population per generation. A leftward shift of the curve’s centre indicates performance improvement, a narrowing width signifies convergence, and a widening width suggests exploration is being maintained. FO1 (1/Stability), FO5 (Total Length), FO6 (Slope): The centre of the distribution shifts distinctly to the left, showing clear performance improvement. Simultaneously, the distributions’ width is maintained or slightly expands, indicating that the algorithm is intentionally preserving the diversity of the solution set while improving performance. FO2 (1/Terrace Area): A slight leftward shift and maintained width show that improvement is slow but diversity is preserved. FO3 & FO4 (1/Travel Distance): The distribution centre shows little to no movement, or even a slight rightward shift. This implies that performance improvement for these two objectives is delayed or being sacrificed for other objectives, and the algorithm continues to explore solutions with diverse travel strategies.

Fig.5.6. Wallacei optimisation process showing Standard Deviation/KDE, Fitness Values, and SD/Mean Trendlines

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(2)

Results: Pareto Front and Trade-offs

After 40 generations of evolutionary computation, we obtained the Pareto Front. In the parallel coordinate plot, each axis represents a normalised objective value (lower is better). A distinct crossover pattern is observed, particularly between the ‘Stability (FO1)’ and ‘Total Length (FO5)’ axes. This graphically illustrates the significant trade-off: solutions with long paths (high FO5 value) tend to maximise stability (low FO1 value), whereas solutions with drastically reduced length (low FO5 value) sacrifice some stability (high FO1 value). The product of multi-objective optimisation is not a ‘single correct answer’ but a portfolio of optimal alternatives representing different values.

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Fig.5.7. Iterations of Clusters 1 and 2.

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(3) Analysis of the Solution Set using Unsupervised Learning: K-Means Clustering (k=4) To make a meaningful choice from the hundreds of solutions on the Pareto front, we used the K-Means clustering algorithm to summarise the solution set into four ‘Design Families’ and identify representative trade-off types. Cluster 1 (Stability-Focused): This cluster showed the lowest values (best performance) on the ‘Stability (FO1)’ and ‘Terrace Area (FO2)’ axes. It represents a strategy that prioritises the physical stability of the site and the creation of usable land. In return, it shows relatively high values for ‘Total Length (FO5)’ and ‘Slope (FO6)’, indicating a willingness to accept increased cost and construction difficulty for the sake of stability.

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Fig.5.8. Iterations of cluster 3 and 4.

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Clusters 2 & 3 (Balanced): These two clusters are distributed around the median values on several axes, representing various forms of ‘compromise’ solutions that aim to satisfy all objectives to a certain level rather than maximising any single one. Cluster 4 (Efficiency-Focused): Conversely, this cluster showed the lowest values (best performance) for ‘Total Length (FO5)’ and ‘Slope (FO6)’. This is a strategy for constructing the corridor with minimal cost and resources. However, it had the highest values for ‘Stability (FO1)’, showing that some stability was sacrificed for cost reduction.

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Fig.5.9. Best Performaing Iterations

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(4)

Final Design Selection

The primary objective of this project is to resolve the fundamental instability of the Jharia site and to establish a safe foundation for long-term ecological restoration and architectural expansion. Therefore, we selected Cluster 1, the design strategy with the highest stability (lowest average value for the ‘Stability (FO1)’ objective function) as the final design direction. This choice was made by prioritising the project’s core value of ‘ensuring stability’, accepting the cost of an increase in ‘MaxTravel Over 40m(FO3)’ and ‘Max Travel(FO4)’.

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Fig.5.10. Final Best Performaing Iteration.

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(5)

Conclusion

This simulation performed a consistent multi-objective optimisation by converting all objectives into minimisation problems. The resulting graphs demonstrated both clear performance improvements in key objectives (FO1, FO5, FO6) and a balanced exploration amidst complex trade-offs with other objectives (FO3, FO4). Finally, by combining the Pareto front with K-Means clustering, we derived meaningful ‘Design Families’ and selected the ‘Stability-Focused’ cluster in alignment with the project’s core values. This demonstrates a practical workflow that enables quantitative and transparent decision-making beyond the designer’s subjectivity.

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Stage 1: Suitability Filtering The first task is to select only those species able to survive the extreme conditions of each patch. The script iterates through all patches composing the corridor and reads their environmental data established in 4.1, including slope, risk value and the presence of bedrock. It then compares these values with the ecological thresholds specified in the Jharia plant database for each species, such as slope range and pollution tolerance. If a patch’s slope lies outside a species’ tolerance or contamination exceeds what the species can withstand, that species is removed from the patch’s candidate list. In this way, the large database is reduced to a shortlist of survivable species tailored to each patch. Stage 2: Adaptive Guild Allocation With the survivable list prepared, the script diagnoses the patch’s most pressing problem and prescribes an appropriate combination of guilds. For example, if elevated risk is driven primarily by steep slope and the candidate list contains many Anchor-guild species, physical stabilisation is prioritised. A dynamic guild recipe is then generated, for instance allocating more than 60 per cent of the notional 3 × 3 planting grid to the Anchor guild. If remediation of contamination is more urgent, the proportion of the Detox guild increases; if soil fertility must be improved, the Builder guild is given greater weight. Each patch thus receives a customised guild mix matched to its dominant constraint. 5.1.4 Ecological Planting Strategy: The Guild Logic

The optimised stabilisation corridor has established the physical and social structure of the landscape; next, the project proceeds to the ecological design phase, imbuing it with life. Manually designing optimal plant assemblages for the unique conditions of tens of thousands of soil patches is infeasible. Accordingly, to translate the guild strategy and plant database developed in 4.2.1 into an implementable scheme, an automated intelligent planting algorithm was implemented in C#. The script functions not as a simple filter but as a design instrument that diagnoses each patch and prescribes tailored ecological treatments. The algorithm follows a three-stage logic.

Stage 3: ‘Diversity First, Dominance Supplemented’ Assembly Species are then assigned to planting slots according to the guild recipe. To support long-term ecological resilience, the algorithm applies a diversity-first principle, seeking to fill slots with as many distinct species as possible without duplication, thereby maximising genetic and functional diversity against environmental shocks and disease. If the number of available species is insufficient to fill all slots, the remaining positions are completed according to a dominance-supplemented rule by repeatedly placing the highest-dominance species within the relevant guild, securing initial stability and stand density. Through this three-stage automated process, the corridor is transformed into a fine-grained mosaic of microhabitats, each encoding an optimal ecological response to its localised problems. 131

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Fig.5.11. Layers for ecological simulation. 132

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Stabilising plants

Phytoremediation plants

Soil-Enriching plants

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Fig.5.12. Plant Type 133

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This diagram illustrates how the overall ecological corridor strategy translates into a detailed planting logic. Starting from the map showing the large scale of the intervention, a specific grouping is enlarged to reveal a more detailed subdivision of the terrain, with each 5x5m patch divided into 3×3 plots. Each plot functions as a data-driven unit, where the intelligent system prescribes plant information based on plant type. The layered diagram on the right shows how this process unfolds: first, patches within the corridor are identified, then grouped, and finally assigned specific plant types based on their stabilisation, remediation, soil enrichment, or biodiversity functions. This step-by-step logic demonstrates how the data information obtained (risk, water movement, contaminated soil, and others) is based on spatial accuracy.

Fig.5.13. Map of the corridor with ecological data. 134

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Fig.5.14. Diagram of layers of the ecological data into the corridor. 135

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5.2 Ecological Planting Strategy: Ecologic

In order to evaluate the plant growth and hence the biomass created in this otherwise degraded land by targeted planting across the corridor, and within cultivation areas identified for remediation, we used Rhino Ecologic. The tool integrates site geometry, planting zones, and species profiles (such as indicative growth rates, canopy expansion, rooting depth, and soil-building traits) to simulate time-stepped changes in vegetation and soils. Using this workflow, we tested a 100 m wide segment of the selected cluster. Simulations were run for 2, 5, 6, and 9 years to estimate gains in soil organic biomass and plant volume created. The plant palette reported here reflects species extracted from the experiments, and the results are presented as spatial maps and summary metrics for each time period.

10 5.2.1.1 Ecological Modelling: Stabilization Corridor

As per the allocation of planting species in each patch of the stabilization corridor, plant species are extracted for the ecological modelling experiment. These species are distributed in a voxel resolution of 2 x 2 meter depending on their growth radius and other data prescribed in the algorithm. The existing soil condition, sun radiation and inclination play a factor in the simulation of the plant growth, We then see in time steps how much plant volume and biomass is generated in the area. The initial years show less plant density but in a period of 7 to 9 years increased biomass is recorded. This can reflect that that choice of species and their resultant biomass can improve the soil conditions and the biodiversity in an otherwise degraded land. The inclination as seen in the diagram shows some steep and flat regions which implicate plant distribution however the slope conditions are suitable where plant growth is still possible.

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Planting Allocated in the region Eulaliopsis binata Vetiveria zizanoides Azadirachta indica Butea monosperma Casuarina equisetifolia Holarrhena pubescens Ziziphus mauritiana Acacia auriculiformis Albizia lebbeck Cassia siamea Leucaena leucocephala Dalbergia sissoo Prosopis juliflora Thevetia peruviana

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Lantana camara Calotropis procera Sesbania bispinosa Senna sophera Ricinus communis Crotalaria juncea Mimosa pudica Desmodium triflorum Parthenium hysterophorus Cynodon dactylon Spirodela polyrhiza Lemna minor Pistia stratiotes Eichhornia crassipes

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Fig.5.15. Ecological Modelling for Corridor

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5.5.1 Ecological Modelling: Remediation Zone

While the ecological corridor houses selective species based on micro condition, the rest of the site undergoes rounds of planting with specific phyto remediation plants that woudl aid in impriving soils conditons and remove toxins. These areas are later predicted to house eco culture activities and cultivation zones. here only a select few species are simulate and again the maximum increase in plant volume occurs in the first 2-5 years and hence signifying how even in the current soil conditions the plants can perform and improve the soil. 0d

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Planting Allocated in the region Vetiveria zizanioides Calotropis procera Lantana camara Cynodon dactylon Eulaliopsis binata Eichhornia crassipes Pistia stratiotes Lemna minor Spirodela polyrhiza Acacia auriculiformis Cassia siamea Dalbergia sissoo Leucaena leucocephala

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Fig.5.16. Ecological Modelling for Furture Cultivation Areas

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Fig. 5.17. Diagram of layers of the ecological data into the corridor.

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Stage 1: Filtering for buildable ground The script first retains only those patches, among the thousands provided as input, that qualify as buildable ground. As a first pass, only patches with slope angles of at least 10 degrees are kept, securing a minimal gradient that benefits drainage and outlook, while very flat low-lying areas are excluded. The surviving patches are then divided, using the risk score computed in 4.1.1, into two classes. Patches scoring 0.6 or higher are designated as retaining-wall areas (Red Zone) that require urgent structural reinforcement. Patches scoring between 0.2 and 0.6 are designated as potential residential areas (Yellow Zone). This stage establishes basic buildability. Stage 2: Formation of Foundation Groups (the basic unit of construction) Filtered residential patches are grouped into Foundation Groups, the basic unit for construction, under three strict rules. First, under a horizontal-first rule, only patches lying within a ‘Z Tolerance’ threshold of near-equal elevation are eligible for grouping, which avoids unrealistic podiums and ensures stable foundations. Second, under a score-first rule, grouping begins with higher-risk patches among those at the same elevation, reflecting the need for stabilisation. Third, under a size-limit rule, each group contains only a bounded number of patches, controlled by MinGroupSize and MaxGroupSize parameters, for example 2 to 4. This step reorganises dispersed candidate sites into buildable modules. Stage 3: Formation of Clusters (residential compounds) Hundreds of Foundation Groups are then aggregated into larger clusters using the K-Medoids algorithm. The medoid of each cluster is not an abstract coordinate but an actual Foundation Group located on an existing road, which guarantees realistic internal accessibility. RandomSeed is fixed so that rerunning the script yields identical clustering and preserves process consistency. Stage 4: Automatic calculation of household numbers (production-based)

5.2.The Settlement: An Algorithmic Masterplan 5.2.1 The Clustering Logic for a Productive Settlement

The optimised corridor formed the macro structure of landscape, moving into an algorithmic masterplan to deploy tangible programs for human activities and life. The next step allocates the multilayered programme proposed in 4.3.1 to actual space through a data-driven C# script rather than subjective judgement. The automated site-selection script derives optimal building plots from complex terrain data, sets residential density from productive yield, and finally places buildings while reflecting design intent. It follows a five-stage logic.

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Residential density for each cluster is coupled directly to the area of ‘Terrace Areas’. Each terrace is assigned to the nearest cluster. The total cultivated area assigned to a cluster is divided by ‘Area Per Household’ to compute the maximum number of households that the cluster can support. This embeds socio-ecological sustainability into the masterplan. Stage 5: Final building selection (hybrid method) Buildings are placed within each cluster up to the number computed in Stage 4. A hybrid priority score combines ground quality (Score) and accessibility (Proximity to Medoid). The designer adjusts SelectionWeight from 0.0, indicating accessibility only, to 1.0, indicating ground quality only, in order to shift the priority between communal interaction and individual amenity. This enables rapid and flexible exploration of settlement scenarios grounded in explicit data.

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Fig. 5.18. Diagram of layers of the ecological data into the corridor.

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The information resulting from the previous experiment is used. The corridor with Risk Data information and also the resulting area of cultivation zones. Based on the risk map, we identified the lowest-risk areas as potential sites for the architecture, while also delineating areas for cultivation. Following Indian housing standards, where a dwelling is defined as 2,000 m², the available area of the corridor allows for the definition of 10 groups, each of which varies in size, resulting in mostly clusters of 36 dwellings and up to 45 in other cases.

Fig.5.17 : Diagram of layers of the ecological data into the corridor.

Fig.5.19. Diagram of layers of the ecological data into the corridor.

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Fig.5.20. Diagram of layers of the ecological data into the corridor.

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Fig.5.21. Clustered Zones and typologies

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Fig.5.22. View of Cluster of potential architecture zones and structures

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5.3 The Structure : An Integrated Eco-Structural System

This chapter introduces the design logic behind the bio-receptive retaining structure proposed for the corridor. The intention was to develop an element that combines structural stability with ecological potential. The structure is conceived as a hybrid system, one that responds to ground conditions, supports plant colonisation through porosity, and maintains the performance required to stabilise degraded terrain. Rather than placing vegetation as a secondary layer, here the wall itself is designed to become a host. Its porosity allows for the embedding of soil pockets, root systems and microclimates, enabling species to take hold and gradually transform the structure into an ecological agent. At the same time, the wall must remain structurally competent, especially in the more unstable areas of the corridor where terrain movements and lateral loads are expected. This chapter sets the foundation for understanding how material, geometry and ecological function come together in a single structural element. The following pages explore the parameters that shape this hybrid behaviour and introduce early design considerations for its integration in the corridor.

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5.3.1 Ground - Structure Interaction

In order to demonstrate the design of the structure one cluster is identified post the settlement experiment. Clear zones are established in that experiment to define areas to intervene solely for stabilization purpose and areas for future architectural interventions. While the development of the Architecture lies in the M.Arch phase here the necessary foundation points and load conditions are considered in response to the slope and structure. Going forward the diagrams explain the decision making and workflow behind the formation of the structure. This is a conclusive logic post the initial form- finding experiments conducted.

Fig.5.24. Identified Cluster 150

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About a 15m patch identified in the corridor. The slopes inclinations vary from 0 to 60 deg in the corridor, however here it is about 35 deg.

This area becomes the extent of the structure. Injection grouting technique learnt from initial case studies informed the pile foundations that combine the need to inject inert material into the subsiding cracked grounds.

Load cases from architecture and forces exerted from the earth that is retained under necessary pile supports lead to stress flows. These stress lines identify necessary zones of the material for the transfer of load. Fig.5.25. Bio-receptive structure stages of formation 151

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Based on the stress lines generated, zones are identified to subtract material, forming places for planting, which are created by Voronoi cells that adjust to the densities defined by the lines. Low stress

A pattern emerges for creating planting pockets leading to a subtraction of material in the identified zones. This strategy alters the shell to while ensuring necessary force flow. This forms the bottom layer of the structure.

Planting pockets are combined along the length of the structure to form larger planting pockets.

Fig.5.26. Bio-receptive structure stages of formation-02 152

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This is material alteration is again evaluated for structural stability. The displacement

This connected pockets become place for soil partially embedding the structure underground.

Plants roots intertwined around this double layered system over time anchoring the structure to the ground. Larger the pockets larger the plants. Plants are distributed based on the planting data assigned to this micro condition of the corridor. Fig.5.27. Bio-receptive structure stages of formation-03 153

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5.3.2 Multi-objective Optimization

This base geometry is tested using the multi-objective optimization process using Wallacei where multiple objectives are established that compete to lead to an optimized geometry. Considering the material properties it is necessary to reduce tension zones in the structure hence the location of support points play a crucial role. The overall displacement is minimized after creating the necessary subtractions and additions of material for planting pockets. The bottom layer that ensures load transfer is maximized while the top layer connection and pockets are maximized for maximum plants to be integrated. The overall material volume is also minimized.

Minimize Tension FC01

Minimize Displacement FC02

Maximize Bottom connection FC03

Maximize Size of Planting Pockets FC04

Minimize Material Volume FC05

Fig.5.28. Wallacei Objectives 154

Fig.5.29. Wallacei Parallel Co-ordinate Graph, Pareto Clusters, SD Graphs

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Fig.5.30. Chosen iterations from best performing and clustered pareto-fronts 155

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Fig.5.30. Chosen Iteration

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5.3.2 Fabrication and Construction Fabrication of the the bio-receptive structure is conducted using a robotic in-situ 3D printing technique. The structure is segmented in approximate 15 meter considering the reach and limits of current robotic techniques. Based on the site conditions the robot is placed in proximity to the printing location. Material deposition is carried out with continuous supply of pre-mixed material which can be supplied from nearby suitable location. The designs offer minimal site alteration required except for drilling the locations for injection grouting that help in ground subsidence and also anchoring the structure to the ground. First the injection grouting is carried which is a proven technique for ground stabilization with the same material to the structure. This leads to a homogenous material continuation in the structure and its anchor even for high slope areas. Construction of each

structure would take approximately 24 hours but this would vary based on rate of extrusions and speed of printing that can be adjusted for suitability. The toolpath movement for the robotic arm is non-planar movement meaning the first layer of the structure is in contract with the ground and rises upwards. Two ways of slicing were considered in the experiment and this technique was taken forward. This way of movement of the printing ensures connectivity to the ground making it viable structurally as well as least hindrance of the ground for the robotic extruder. Also this means the geometry has no overhangs when voids for the planting that vary in sizes and angles. But this printing is carried out along the voids.

Fig.5.32 ToolPath Generated for Rotbot Patch movement using Ai Build Platform 157

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5.4 Final Extrusion

Extrudability tests on sloped terrain showed that the mix could be deposited with steady flow and structural stability, even under gravitational stress. The nozzle maintained continuous extrusion without clogging or collapse, producing uniform layers with strong adhesion to the substrate. At steeper gradients, slight deformation revealed the sensitivity of the system to water content and binder ratios, but these effects were controlled by adjusting mix viscosity and print speed. Overall, the trials confirmed that the material is not only printable in controlled environments but also adaptable to irregular slopes, underscoring its potential for on-site robotic fabrication in unstable landscapes such as Jharia. The base material was ground down to reduce particle size and improve homogeneity, after which the ground material was sieved to ensure finer and more uniform particles suitable for testing and extrusion. To this refined base, 5% alginate and locust bean gum were added as natural binders, aimed at enhancing cohesion, viscosity control, and bio-receptivity. All components were then thoroughly mixed, with close observation of rheology, including how the mixture flows, holds shape, and resists deformation. Finally, the prepared mixture was extruded through a 3D printing nozzle, forming test structures for evaluation of printability and stability.

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Fig.5.33. Material Preparartion

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Fig.5.34. Stages of Fabrication

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VI DESIGN PROPOSAL The ‘Research Development’ chapter analysed terrain phenomena and decoded its potential, while this ‘Design Development’ chapter synthesises those results to generate and optimise tangible spatial and material solutions. It constitutes a process that actively explores and advances design using data as inputs, forming the core of the project’s computational design workflow. The chapter documents how the design is progressively specified along four axes: system, settlement, material, and structure. First, the System part details the implementation of the complex behavioural logic of agent-based modelling and traces the full procedure by which a multi-objective optimisation algorithm evaluates thousands of alternatives to derive an optimal stabilisation-corridor master plan. Second, the Settlement part applies algorithmic clustering to this master plan and arranges a concrete spatial programme for productive settlement. Third, the Material part verifies material performance through physical prototyping and establishes robotic 3D-printing fabrication strategies for constructing on irregular, sloping ground. Finally, the Structure part completes a living eco-structural system by means of C# logic that integrates structural modelling, soil–structure interaction, and ecological porosity. Taken together, these stages demonstrate that the final design proposal is not a merely formal outcome but the logical consequence of a rigorous, multi-scalar problem-solving process.

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6.1 The Masterplan : Integrated Stabilisation Corridor

6.1.1 Final route and priority segments

The backbone of the final master plan is the stability-focused stabilisation corridor derived from multi-objective optimisation in Chapter 5.1.3. This route was selected from among thousands of alternatives as the solution that most effectively addresses the site’s fundamental problem of physical instability while remaining balanced in terms of terrace-area provision, path length and gradient. It is not a simple straight line connecting the most hazardous points; rather, it is an organic and efficient path generated through the complex exploratory process of agent-based modelling.

Fig.6.1. Risk Map

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Fig.6.2. Risk-Map Zoomed

The risk formula developed in 4.1.1 was then reapplied exclusively to the patches that make up this final corridor, producing a detailed linear Corridor Risk Map across its entire extent. On the basis of this map, the corridor is divided into three zone types, each carrying a different level of intervention. Segments with risk values of 0.7 or higher, the Red Zone, constitute the highest-priority intervention areas, where active structural measures such as the bio-receptive retaining walls designed in Chapter 5.4 are concentrated. These segments aim to prevent further collapse and to act as structural anchors for the wider system. Segments with values between 0.4 and 0.7, the Yellow Zone, provide the groundwork for potential settlement and productive activity, where structures combined with buildings integrate stabilisation with programme. Segments below 0.4, the Green Zone, minimise structural works and focus on vegetative restoration, serving as ecological nodes and buffers for the corridor as a whole. In this way, the master plan allocates limited resources most effectively through differential strategies grounded in data, and delivers tailored solutions suited to the specific conditions of each locality.

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6.1.2 Hydrology and treatment coupling

The stabilisation corridor functions as living ecological infrastructure that actively reorganises and enhances the site’s hydrological regime. The monsoon climate of Jharia alternates between intense downpours and dry seasons, accelerating soil erosion and water pollution. Within this master plan, the corridor itself forms the boundary of a large terraced landform. The extensive terrace fields delineated by the corridor act as first-order stormwater infrastructure: during heavy rain they slow overland flow, attenuate peak discharge, and provide broad surfaces for retention and infiltration, allowing rainfall to percolate gradually into the ground. This reduces flashflood risk, conserves topsoil otherwise lost to erosion, and, over the long term, contributes to groundwater recharge by replenishing depleted aquifers. The corridor operates as the central conduit and an active water-distribution system. Surplus runoff not absorbed by individual terraces is conveyed into channels integrated within the corridor structure. These channels do more than drain water; guided by hydrological analysis, they deliver flows to strategic receiving points. Water is routed along the corridor to nature-based treatment systems proposed in 4.3.1, such as constructed wetlands. There, aquatic species from the **Detox** guild, densely planted according to the algorithm in 5.1.4, absorb and filter heavy metals and acidic effluents originating from upstream spoil heaps. The treated water is then recirculated through the corridor for downstream agricultural and ecological uses, establishing a closed-loop water-management system that enhances the self-sufficiency and sustainability of the whole. In this way, the master plan integrates structural stabilisation with aquatic-ecosystem restoration as a single, coherent system, holistically restoring the health of the landscape.

Fig.6.3. Diagram of layers of the ecological data into the corridor.

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Fig. 6.4. Diagram of layers of the ecological data into the corridor.

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6.2 The Structure

Fig 6.5. Section A

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Fig 6.6. Plan of Bio-receptive Structure

The structure overtime is inhabited by plants and their roots intertwine between with the material

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Fig 6.7. Section B

Fig 6.8. Section C

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The sectional drawings of the bio receptive structure with integrates planting has various degrees of porosities and hence planting palettes. The plants facilitate in soil stabilization along with aiding in anchoring the structure over time adding strength and hence when housing typologies start adapting the structure an overall stabilized ground condition is created.

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Fig 6.9. Multiple Typologoes as per different ground conditions and strcutural requirement

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Fig 6.10. Cluster with potential stabilization zone and foundation for furture architectural intervention

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6.3 THE PALIMPSEST A palimpsest is never erased but layered; in Jharia, the scars of mining become the foundation for growth, where walls, plants and dwellings inscribe a new text upon the land.” The concept of palimpsest has its origins in ancient manuscripts, especially medieval scrolls, in which previous texts were carefully scraped or erased to make spaces for new inscriptions. However, the remains of the previous words never disappeared completely, but remained beneath the surface, tenuous but present, creating a condition of stratified time in which memory and transformation coexisted. In Jharia, the mining landscape embodies this same condition. The scars of a long-degraded, unstable and contaminated terrain generate layers of degradation and risk. Rather than attempting to erase these traces, the project embraces them, conceiving regeneration as an act of inscription upon what already exists. The ecological corridor becomes the new manuscript, a framework that starts from instability and superimposes it with systems of remediation, cultivation and habitability. Each phase of this palimpsest does not overwrite the previous one but builds upon it. Bio-receptive 3D-printed structures stabilise the terrain, cleansing and anchoring the ground; adaptive planting species introduce ecological processes that detoxify and enrich the soil; cultivated fields transform remediated land into productivity; and architecture emerges as an extension of these living systems. The regenerative strategy for Jharia is not conceived as a single event, but as a temporal process that unfolds through successive layers of transformation. It draws directly on information generated by previous experiments, in which data-driven explorations, risk corridors, plant type allocation corridors, and patch classification lay the groundwork for intervention. These analytical layers are not an end in themselves, but rather the basis for a phased evolution in which each stage materialises abstract data into tangible actions: the printing of biorceptive structures, the introduction of ecological systems, the cultivation of productive land, and the gradual emergence of dwellings. In this way, the palimpsest is not only a metaphor, but also a method. It frames design as a temporal strategy, recognising the pre-existence of mining activity and superimposing processes of remediation, cultivation, and architecture. Rather than erasing the past, the project inscribes a new narrative of resilience, fertility, and development directly onto the scars of extraction.

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PALIMPSEST - Phased Growth: Architectural Expansion Over Time A Speculative Vision for a Post-Mining Palimpsest

Fig 6.11. Dagram showing different stages of intervention and alteration of land

Phase 1 – Years 0–5: Stabilisation and Foundations

Phase 2 – Years 5–10: Ecological Implantation

The process begins with the robotic inscription of 3D-printed bio-receptive structures. These are placed within the corridor in patches identified by the algorithm as key sites for stabilisation. Using previously developed materials research that transforms mining waste into printable, porous, and bioreceptive material, the robots establish the first physical layer of the palimpsest. These structures are not dividing walls, but anchoring systems designed to hold the ground, capture water, and prepare the soil for future ecological remediation.

Once the initial structures are in place, ecological processes are introduced through adaptive planting. Guided by the plant allocation algorithm, species are selected according to their function: some stabilise the soil with deep roots, others remediate it by absorbing pollutants, while others enrich its fertility. At this stage, the landscape is not yet productive, but it is recovering: the soil is being detoxified, compacted and progressively restored.

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Phase 3 – Years 10-15: Cultivation and Productivity

Phase 4 – Years 15+: Settlement Integration

As the terrain stabilises and the soil regains its capacity, terraces for cultivation emerge along the corridor. The first crops are not intended for consumption, but rather for soil regeneration: cover crops and nitrogen-fixing species that accelerate fertility. Once the soil recovers, agricultural production begins to take shape. At this point, agriculturalrelated architectural structures appear: research centres, water reservoirs, and small community hubs that organise production clusters.

Once stability has been achieved and crops have flourished, architecture emerges as an extension of these systems. Homes appear progressively, distributed according to the grouping logic defined by the patches of the corridor. Community buildings and homes generate clusters, each with its own defined farmland. In this way, the home becomes the final inscription on the palimpsest, a layer that does not erase the scars of mining, but rather redefines them as foundations for resilience and renewal.

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Fig.6.12. Phased Growth- The Palimpsest

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Fig.6.13. View of the Bioreceptive Structures 182

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Fig.6.14. View of the intervened terrain 184

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VII

CONCLUSION 7.1 Discussion of Research and Contributions

The core contribution of this research is the proposal of a datadriven, comprehensive computational design workflow and the demonstration of its feasibility in addressing the complex and unpredictable challenges posed by devastated postmining landscapes. This represents overcoming the limitations of the top-down and form-centred approaches in traditional architectural design, while proposing a new design paradigm that integrates systematic analysis, generative design, multi-objective optimisation, and digital fabrication into a single organic feedback loop. To be specific, this research contributes in four distinct aspects. First, it proposes a risk-modelling methodology that quantitatively analyses potential risks and opportunities by integrating fragmented data through a systematic approach. Building on this foundation, an optimally balanced master plan is derived by negotiating trade-offs among stability, efficiency and social connectivity through agent-based modelling (ABM) and multiobjective optimisation (MOO), thereby demonstrating a rational and transparent decision-making process for complex, large-scale problems. Second, in pursuit of an eco-centred design, an Intelligent Planting Algorithm is developed that reclassifies plants into functional guilds and prescribes optimal plant communities according to the inherent environmental conditions of each soil patch. Rather than treating ecological restoration as a peripheral element of the design process, this approach positions it as a core driver for the generation of form and programme. Third, with respect to material reuse and functional extension, a bio-receptive composite is developed using local industrial waste, combining structural stability with ecological performance. This suggests a new trajectory in which architectural materials become living substrates that interact with plants and microorganisms and are capable of adaptive evolution. Finally, through intelligent fabrication, a practical construction strategy is established by combining robotic 3D printing with non-planar toolpath algorithms, enabling the precise realisation of complex digital designs on hazardous, irregular terrain where human access is limited. In conclusion, by interlinking the logics of system, ecology, material, structure and fabrication into a single integrated digital chain, the research demonstrates that architecture can operate not merely as object design but as a powerful process for healing and reconfiguring complex socio-ecological systems.

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Fig.6.15. Visualisation of the Stabilisation corridor across the terrain

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Fig.6.16. Visualisation of the planting pockets

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7.2 Limitations and Future work

This research presents the capability of an integrated computational design workflow, but also contains explicit limitations. First, this research mainly depends on digital simulation and lab-scale prototyping. The long-term durability of the developed materials, their bio-receptive performance under real climatic conditions, and the successional dynamics of plant communities require verification through extended in-situ trials. Second, the work lacks an in-depth analysis of the socio-economic feasibility of the proposed system. Future research should investigate the initial expenses of construction, the frameworks for long-term operational upkeep, and the governmental and administrative frameworks that can ensure the engagement and approval of the community. Third, the study is closely tied to the specific context of Jharia. To demonstrate the generalisability of the workflow, comparative applications are needed across postmining sites worldwide that exhibit different geological, climatic and social conditions. These limitations naturally indicate directions for future research. First, full-scale prototypes (1:1 mock-ups) with long-term monitoring should be undertaken to validate the real-world performance of the proposed materials and structural systems, narrowing the gap between digital models and physical reality and advancing construction details. Second, the conceptually proposed programme and settlement scenarios will be developed in the subsequent M.Arch phase into concrete architectural design, exploring how bio-receptive structures can become inhabitable space and new architectural typologies. Third, the computational design workflow should be further generalised and made more accessible by developing user-friendly interfaces for the underlying scripts, enabling a wider community of designers and planners to participate in data-driven ecological restoration. Ultimately, this research is a beginning rather than an end. It offers a methodological proposition for building new hope on devastated ground, to be strengthened through continuing experiments, collaboration and practice.

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Fig.6.17. Visialisation of the intervention 190

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66. Balaguru, P. N., and S. P. Shah. Fiber-Reinforced Cement Composites. New York: McGraw-Hill, 1992. 67. Jones, Michael, et al. “Designing Living Architecture: Soil and Compost as Active Materials in Building Systems.” International Journal of Architectural Computing 18, no. 2 (2020): 133–150. https://doi. org/10.1177/1478077120926659. 68. Oxman, Neri. “Material Ecology.” Journal of Design and Science 1 (2016). https://doi.org/10.21428/7e0583ad. 69. Vatanparast, S., A. Boschetto, L. Bottini, and P. Gaudenzi. “New Trends in 4D Printing: A Critical Review.” Applied Sciences 13, no. 13 (2023): 7744. 70. Lee, JaeMyung, and BooMee Park. “A Study on EcoFriendly Materials for 3D Printing – Focused on Korean Hwangto (Loess).” Journal of Asian Architecture and Building Engineering, published online September 9, 2024. 71. Duan, Wenbo, Shunbo Zhao, Linbing Wang, Wensheng Wang, Shaopeng Wu, and Yiyu Lu. “Development of LowCarbon Cementitious Composites with Soil and Industrial Solid Wastes for 3D Printing Construction.” Construction and Building Materials 398 (2024): 132820. 72. Akemah, Emmanuel, and Leeor Ben-Alon. “Mechanical and Rheological Roles of Plant Fibres in 3D Printed Earthen Materials.” Journal of Building Engineering 72 (2023): 106520. 73. D’Alessandro, Antonio, Marco L. Ruello, and Filippo Ubertini. “Rheology of Fibre-Reinforced Earthen Composites for Extrusion-Based Additive Manufacturing.” Construction and Building Materials 291 (2021): 123264. 74. Perrot, Alain, Didier Rangeard, and Alban Pierre. “Structural Built-Up of Cement-Based Materials Used for 3D-Printing Extrusion Techniques.” Materials and Structures 49 (2016): 1213–1220. 75. Siddique, Rafat. “Utilization of Fly Ash in Construction: A Review.” Resources, Conservation and Recycling 54, no. 12 (2010): 1043–1051. 76. Walker, Peter, and Tony Stace. “Properties of Some Cement Stabilised Compressed Earth Blocks and Mortars.” Materials and Structures 30 (1997): 545–551. 77. Bhusal, Sushil, Markus Schmid, and Mateusz Malinowski. “Lime-Stabilised Earthen Materials: Brittleness and Mechanical Performance in Extrusion Contexts.” Construction and Building Materials 365 (2023): 130167. 78. Pacheco-Torgal, Fernando, Said Jalali, António F. Marques, and Jalal Barros. Eco-Efficient Construction and Building Materials: Bioreceptive Materials and Biochar Applications. Cambridge: Woodhead Publishing, 2014. 79. Prieto, Ana, Rubén Hernández-Córdoba, and José Antonio Lozano-García. “Bio-Based Additive Manufacturing: Prospects for Microbial Colonisation and Ecological Performance.” Journal of Cleaner Production 258 (2020): 120578. 80. Barrare, Jharkhand, 2024. 81. Bhattacharya, P., and N. Chakraborty. “Soil Compaction

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APPENDIX

Appendix A: C# Script for the Clustering Algorithm 1 Script Overview

2 Code Structure

This script runs in the Grasshopper environment and automatically selects buildable plots from terrain patch data, then clusters them into settlement-scale units. Using each patch’s risk Score and slope Angle, the script classifies patches into Red Zone (unsuitable for building) and Yellow Zone (buildable). Adjacent Yellow Zone patches are grouped to form Foundation units, which are subsequently aggregated into a small number of larger clusters using the K-Medoids algorithm. For each cluster, the potential household count is derived from the area of nearby agricultural terraces (Terrace Area). Final plot selection is performed by weighting risk score and proximity to the cluster medoid, producing a priorityranked set of building sites. Outputs include selected plots, cluster geometry and all associated metadata, formatted for Grasshopper data trees.

The code consists of a main RunScript method that orchestrates the workflow, together with supporting classes for data handling and the core algorithms.

RunScript method Input validation: checks for missing fields and malformed inputs. Filtering and classification: filters patches to criteria and labels them as Red or Yellow Zones. Adjacency grouping: merges adjacent Yellow Zone patches into Foundation groups. K-Medoids clustering: aggregates Foundation groups into K clusters. Household estimation and final site selection: assigns nearby Terrace Area to clusters, computes potential household numbers, and selects final building plots via a weighted evaluation of risk score and proximity to the medoid. Outputs: emits selected sites, cluster information and diagnostic data to Grasshopper.

Supporting classes and functions PatchInfo, FoundationInfo: structured containers for single patches and grouped Foundations, storing geometry, Score, Angle and zone labels. KMedoids, Cluster: implementation of K-Medoids clustering, ensuring medoids correspond to actual Foundation groups rather than abstract centroids. GroupAdjacentPatches(): adjacency-driven search that builds Foundation groups from neighbouring Yellow Zone patches.

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SelectTopFoundations(): ranks candidate Foundations within each cluster using a weighted combination of risk score and medoid proximity, returning the highest-priority building plots.

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using System; using System.Collections; using System.Collections.Generic; using Rhino; using Rhino.Geometry; using Grasshopper; using Grasshopper.Kernel; using Grasshopper.Kernel.Data; using Grasshopper.Kernel.Types; using System.Drawing; using System.Linq; using Rhino.Geometry.Intersect;

/// <summary> /// This class will be instantiated on demand by the Script component. /// </summary> public class Script_Instance : GH_ScriptInstance { #region Utility functions /// <summary>Print a String to the [Out] Parameter of the Script component.</summary> /// <param name=”text”>String to print.</param> private void Print(string text) { /* Implementation hidden. */ } /// <summary>Print a formatted String to the [Out] Parameter of the Script component.</summary> /// <param name=”format”>String format.</param> /// <param name=”args”>Formatting parameters.</param> private void Print(string format, params object[] args) { /* Implementation hidden. */ } /// <summary>Print useful information about an object instance to the [Out] Parameter of the Script component. </summary> /// <param name=”obj”>Object instance to parse.</param> private void Reflect(object obj) { /* Implementation hidden. */ } /// <summary>Print the signatures of all the overloads of a specific method to the [Out] Parameter of the Script component. </summary> /// <param name=”obj”>Object instance to parse.</param> private void Reflect(object obj, string method_name) { /* Implementation hidden. */ } #endregion #region Members /// <summary>Gets the current Rhino document.</summary> private readonly RhinoDoc RhinoDocument; /// <summary>Gets the Grasshopper document that owns this script.</summary> private readonly GH_Document GrasshopperDocument; /// <summary>Gets the Grasshopper script component that owns this script.</summary> private readonly IGH_Component Component; /// <summary>t /// Gets the current iteration count. The first call to RunScript() is associated with Iteration==0. /// Any subsequent call within the same solution will increment the Iteration count. /// </summary> private readonly int Iteration; #endregion /// <summary> /// This procedure contains the user code. Input parameters are provided as regular arguments, /// Output parameters as ref arguments. You don’t have to assign output parameters, /// they will have a default value. /// </summary> private void RunScript(List<Brep> Patches, List<double> Scores, List<double> Angles, List<Curve> TerraceAreas, double ZTolerance, int MinGroupSize, int MaxGroupSize, int ClusterCount, double SelectionWeight, int RandomSeed, double AreaPerHousehold, ref object RedZonePatches, ref object YellowZonePatches, ref object RetainingWallGroups, ref object RetainingWallIndices , ref object FoundationGroups, ref object FoundationGroupIndices , ref object ClusteredFoundations, ref object OwnedTerraceAreas, ref object SelectedBuildings, ref object SelectedBuildingIndices, ref object ClusterCenters, ref object Log) { // --- 1. Input Validation and Initialization --// This section checks if the provided inputs are valid and initializes data structures. var log = new List<string>(); log.Add(“=== Input Data Validation ===”); log.Add(string.Format(“Input Patch Count: {0}”, Patches != null ? Patches.Count : 0)); log.Add(string.Format(“Input Score Count: {0}”, Scores != null ? Scores.Count : 0)); log.Add(string.Format(“Input Angle Count: {0}”, Angles != null ? Angles.Count : 0)); log.Add(string.Format(“Input Terrace Area Count: {0}”, TerraceAreas != null ? TerraceAreas.Count : 0)); log.Add(string.Format(“Target Cluster Count: {0}”, ClusterCount)); log.Add(string.Format(“Area per Household: {0}”, AreaPerHousehold)); log.Add(string.Format(“Min Group Size: {0}”, MinGroupSize)); log.Add(string.Format(“Max Group Size: {0}”, MaxGroupSize)); log.Add(string.Format(“Selection Weight (Score={0:P0} / Proximity={1:P0}): “, SelectionWeight, 1 - SelectionWeight)); log.Add(string.Format(“Z-Value Tolerance: {0}”, ZTolerance)); log.Add(string.Format(“Random Seed: {0}”, RandomSeed));

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// Initialize outputs to clear any data from previous runs. RedZonePatches = new List<Brep>(); YellowZonePatches = new List<Brep>();

RetainingWallGroups = new DataTree<Brep>(); RetainingWallIndices = new DataTree<int>(); FoundationGroups = new DataTree<Brep>(); FoundationGroupIndices = new DataTree<int>(); ClusteredFoundations = new DataTree<Brep>(); SelectedBuildings = new DataTree<Brep>(); SelectedBuildingIndices = new DataTree<int>(); ClusterCenters = new List<Point3d>(); OwnedTerraceAreas = new DataTree<Brep>(); // Abort if essential data is missing. if (Patches == null || Scores == null || Angles == null || Patches.Count == 0) { log.Add(“ERROR: Input data is null or empty.”); Log = log; return; } // Abort if input list counts do not match. if (Patches.Count != Scores.Count || Patches.Count != Angles.Count) { log.Add(string.Format(“ERROR: The count of Patches ({0}), Scores ({1}), and Angles ({2}) do not match.”, Patches.Count, Scores.Count, Angles.Count)); Log = log; return; } // Abort if group size parameters are invalid. if (MinGroupSize > MaxGroupSize) { log.Add(“ERROR: MinGroupSize cannot be greater than MaxGroupSize.”); Log = log; return; } // Combine all inputs into a single list of ‘PatchInfo’ objects for easier management. var allPatches = new List<PatchInfo>(); for (int i = 0; i < Patches.Count; i++) { allPatches.Add(new PatchInfo(i, Patches[i], Scores[i], Angles[i])); } // --- 2. Filter, Classify, and Analyze --// Filter for patches suitable for building (angle >= 10 degrees). var buildablePatches = allPatches.Where(p => p.Angle >= 10.0).ToList(); log.Add(string.Format(“Buildable patches with angle >= 10 degrees: {0}”, buildablePatches.Count)); // Classify buildable patches into Red (high score) and Yellow (medium score) zones. var redPatches = buildablePatches.Where(p => p.Zone == ZoneType.Red).ToList(); var yellowPatches = buildablePatches.Where(p => p.Zone == ZoneType.Yellow).ToList(); log.Add(string.Format(“Red Zone Patches: {0}, Yellow Zone Patches: {1}”, redPatches.Count, yellowPatches.Count)); // Determine which patches are adjacent to each other for both zones. var redAdjacency = BuildAdjacencyList(redPatches); var yellowAdjacency = BuildAdjacencyList(yellowPatches); log.Add(“Adjacency analysis for Red/Yellow Zones complete.”); // --- 3. Grouping --// Group adjacent patches into ‘retaining walls’ (Red Zone) and ‘foundations’ (Yellow Zone). var retainingWalls = GroupAdjacentPatches(redPatches, redAdjacency, MinGroupSize, MaxGroupSize, ZTolerance); log.Add(string.Format(“{0} retaining wall groups created in Red Zone.”, retainingWalls.Count)); var foundations = GroupAdjacentPatches(yellowPatches, yellowAdjacency, MinGroupSize, MaxGroupSize, ZTolerance); log.Add(string.Format(“{0} foundation groups created in Yellow Zone.”, foundations.Count)); // --- 4. K-Medoids Clustering --// Convert the foundation groups into ‘FoundationInfo’ objects for clustering. var foundationInfos = foundations.Select((group, index) => new FoundationInfo(index, group)).ToList(); // Check if there’s enough data to perform clustering. if (foundationInfos.Count == 0 || ClusterCount <= 0 || foundationInfos.Count < ClusterCount) { log.Add(“WARNING: Not enough foundation groups to perform clustering.”); RedZonePatches = redPatches.Select(p => p.Geometry).ToList(); YellowZonePatches = yellowPatches.Select(p => p.Geometry).ToList(); RetainingWallGroups = ConvertToDataTree(retainingWalls); RetainingWallIndices = ConvertToDataTree(retainingWalls, true); FoundationGroups = ConvertToDataTree(foundations); FoundationGroupIndices = ConvertToDataTree(foundations, true); Log = log; return; } // Perform K-Medoids clustering to group foundations into settlements. var kmedoids = new KMedoids(foundationInfos, ClusterCount, RandomSeed); kmedoids.Run(); log.Add(string.Format(“Clustering of foundation groups into {0} clusters is complete.”, ClusterCount)); // --- 5. Calculate Households per Cluster based on Terrace Area --// This section assigns nearby terrace areas to each cluster and calculates how many // households can be supported based on the total area. var clusterTerraceAreas = new Dictionary<int, double>(); var ownedTerraces = new Dictionary<int, List<Brep>>(); for(int i = 0; i < kmedoids.Clusters.Count; i++)

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{ clusterTerraceAreas[i] = 0; ownedTerraces[i] = new List<Brep>(); } if(TerraceAreas != null) { // Convert terrace curves to surfaces (Breps). var validTerraceBreps = new List<Brep>(); foreach(var curve in TerraceAreas) { if(curve == null || !curve.IsClosed) { log.Add(“WARNING: An input terrace curve was not closed and will be ignored.”); continue; } Brep[] breps = Brep.CreatePlanarBreps(curve, Rhino.RhinoDoc.ActiveDoc.ModelAbsoluteTolerance); if(breps != null && breps.Length > 0) { validTerraceBreps.Add(breps[0]); } else { log.Add(“WARNING: A terrace curve could not be converted to a surface and will be ignored.”); } } // Assign each terrace area to the nearest cluster. foreach(var area in validTerraceBreps) { var areaProperties = AreaMassProperties.Compute(area); if(areaProperties == null) continue; Point3d areaCentroid = areaProperties.Centroid; double minDistance = double.MaxValue; int closestClusterId = -1; for(int i = 0; i < kmedoids.Clusters.Count; i++) { var cluster = kmedoids.Clusters[i]; if(cluster.Medoid == null) continue; double dist = areaCentroid.DistanceTo(cluster.Medoid.Center); if(dist < minDistance) { minDistance = dist; closestClusterId = cluster.Id; } } if(closestClusterId != -1) { clusterTerraceAreas[closestClusterId] += areaProperties.Area; ownedTerraces[closestClusterId].Add(area); } } } // --- 6. Finalize Output Data --// This section prepares the clustered and selected data for output. var clusteredFoundationsTree = new DataTree<Brep>(); var selectedBuildingsTree = new DataTree<Brep>(); var selectedBuildingIndicesTree = new DataTree<int>(); var clusterCenterPoints = new List<Point3d>(); var ownedTerracesTree = new DataTree<Brep>();

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for (int i = 0; i < kmedoids.Clusters.Count; i++) { var cluster = kmedoids.Clusters[i]; if (cluster.Members.Count == 0) continue; var path = new GH_Path(i); // Add all foundation geometries in the cluster to the output tree. var allGeometriesInCluster = cluster.Members.SelectMany(f => f.Patches.Select(p => p.Geometry)); clusteredFoundationsTree.AddRange(allGeometriesInCluster, path); // Calculate the number of households this cluster can support. int householdsForThisCluster = 0; if(AreaPerHousehold > 0) { householdsForThisCluster = (int) Math.Floor(clusterTerraceAreas[cluster.Id] / AreaPerHousehold); } // Select the top foundations based on the household count and selection weight. var topFoundations = SelectTopFoundations(cluster, householdsForThisCluster, SelectionWeight); var selectedGeometries = topFoundations.SelectMany(f => f.Patches.Select(p => p.Geometry)); selectedBuildingsTree.AddRange(selectedGeometries, path); var selectedIndices = topFoundations.SelectMany(f => f.Patches.Select(p => p.OriginalIndex)); selectedBuildingIndicesTree.AddRange(selectedIndices, path); // Store the center point (medoid) of the cluster. if(cluster.Medoid != null) { clusterCenterPoints.Add(cluster.Medoid.Center); } // Store the terrace areas owned by this cluster.

ownedTerracesTree.AddRange(ownedTerraces[cluster.Id], path); log.Add(string.Format(“ - Cluster {0}: Contains {1} foundation groups. Terrace Area: {2:F0} m^2. Calculated Households: {3}. Selected {4} for building.”, i, cluster.Members.Count, clusterTerraceAreas[cluster.Id], householdsForThisCluster, topFoundations.Count)); } // --- 7. Set Outputs --// Assign all processed data to the component’s output parameters. RedZonePatches = redPatches.Select(p => p.Geometry).ToList(); YellowZonePatches = yellowPatches.Select(p => p.Geometry).ToList(); RetainingWallGroups = ConvertToDataTree(retainingWalls); RetainingWallIndices = ConvertToDataTree(retainingWalls, true); FoundationGroups = ConvertToDataTree(foundations); FoundationGroupIndices = ConvertToDataTree(foundations, true); ClusteredFoundations = clusteredFoundationsTree; SelectedBuildings = selectedBuildingsTree; SelectedBuildingIndices = selectedBuildingIndicesTree; ClusterCenters = clusterCenterPoints; OwnedTerraceAreas = ownedTerracesTree; log.Add(“=== Final Result Summary ===”); log.Add(string.Format(“Red Zone Patches: {0}”, redPatches.Count)); log.Add(string.Format(“Yellow Zone Patches: {0}”, yellowPatches.Count)); log.Add(string.Format(“Retaining Wall Groups: {0}”, retainingWalls.Count)); log.Add(string.Format(“Foundation Groups: {0}”, foundations.Count)); log.Add(string.Format(“Clusters: {0}”, kmedoids.Clusters.Count)); Log = log; } // <Custom additional code> // Defines the classification zones for patches based on their score. public enum ZoneType { Red, Yellow, Green } // A helper class to store all relevant information about a single patch. public class PatchInfo { public int OriginalIndex { get; private set; } public Brep Geometry { get; private set; } public double Score { get; private set; } public double Angle { get; private set; } public ZoneType Zone { get; private set; } public Point3d Center { get; private set; } public PatchInfo(int index, Brep geometry, double score, double angle) { OriginalIndex = index; Geometry = geometry; Score = score; Angle = angle; Zone = ClassifyZone(score); var areaMassProperties = AreaMassProperties.Compute(geometry); Center = areaMassProperties != null ? areaMassProperties.Centroid : Point3d.Unset; } private ZoneType ClassifyZone(double score) { if (score >= 0.6) return ZoneType.Red; if (score >= 0.2) return ZoneType.Yellow; return ZoneType.Green; } } // A helper class to represent a group of patches that form a single foundation. public class FoundationInfo { public int Id { get; private set; } public List<PatchInfo> Patches { get; private set; } public Point3d Center { get; private set; } public double AverageScore { get; private set; } public FoundationInfo(int id, List<PatchInfo> patches) { Id = id; Patches = patches; Point3d center = new Point3d(0, 0, 0); double totalScore = 0; foreach(var patch in patches) { center += patch.Center; totalScore += patch.Score; } Center = center / patches.Count; AverageScore = (patches.Count > 0) ? totalScore / patches.Count : 0; } } // --- Adjacency Analysis and Grouping Algorithms ---

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// Builds a dictionary mapping each patch to a list of its adjacent neighbors. private Dictionary<int, List<int>> BuildAdjacencyList(List<PatchInfo> patches) { var adjacencyList = new Dictionary<int, List<int>>(); double tolerance = 0.001; foreach (var patch in patches) { adjacencyList[patch.OriginalIndex] = new List<int>(); } for (int i = 0; i < patches.Count; i++) { for (int j = i + 1; j < patches.Count; j++) { // Check for physical intersection between two patches. Curve[] intersectionCurves; Point3d[] intersectionPoints; bool intersection = Intersection.BrepBrep(patches[i].Geometry, patches[j].Geometry, tolerance, out intersectionCurves, out intersectionPoints); if (intersection && intersectionCurves.Length > 0) { adjacencyList[patches[i].OriginalIndex].Add(patches[j].OriginalIndex); adjacencyList[patches[j].OriginalIndex].Add(patches[i].OriginalIndex); } } } return adjacencyList; } // Groups patches into connected components using a flood-fill (Breadth-First Search) approach. private List<List<PatchInfo>> GroupAdjacentPatches(List<PatchInfo> patchesToGroup, Dictionary<int, List<int>> adjacencyList, int minGroupSize, int maxGroupSize, double zTolerance) { var allGroups = new List<List<PatchInfo>>(); var assignedPatches = new HashSet<int>(); var patchMap = patchesToGroup.ToDictionary(p => p.OriginalIndex, p => p); // Sort patches to ensure a deterministic grouping order, starting from the same Z-level. var sortedPatches = patchesToGroup.OrderBy(p => Math.Round(p.Center.Z, 3)).ThenByDescending(p => p.Score).ToList(); foreach (var startPatch in sortedPatches) { if (assignedPatches.Contains(startPatch.OriginalIndex)) { continue; } var currentGroup = new List<PatchInfo>(); var queue = new Queue<PatchInfo>(); queue.Enqueue(startPatch); assignedPatches.Add(startPatch.OriginalIndex); while (queue.Count > 0) { var currentPatch = queue.Dequeue(); currentGroup.Add(currentPatch); if (currentGroup.Count >= maxGroupSize) { // If the group reaches max size, add remaining items from queue and stop expanding. while(queue.Count > 0) { currentGroup.Add(queue.Dequeue()); } break; } if (!adjacencyList.ContainsKey(currentPatch.OriginalIndex)) continue; var neighbors = new List<PatchInfo>(); foreach (var neighborIndex in adjacencyList[currentPatch.OriginalIndex]) { if (!assignedPatches.Contains(neighborIndex) && patchMap.ContainsKey(neighborIndex)) { // Only add neighbors that are at a similar Z-height. if(Math.Abs(patchMap[neighborIndex].Center.Z - currentPatch.Center.Z) < zTolerance) { neighbors.Add(patchMap[neighborIndex]); } } } foreach (var neighbor in neighbors) { if (!assignedPatches.Contains(neighbor.OriginalIndex)) { assignedPatches.Add(neighbor.OriginalIndex); queue.Enqueue(neighbor); } } } // Only keep the group if it meets the minimum size requirement. if (currentGroup.Count >= minGroupSize) { if(currentGroup.Count > maxGroupSize) { 202

allGroups.Add(currentGroup.GetRange(0, maxGroupSize)); } else { allGroups.Add(currentGroup); } } } return allGroups; } // --- K-Medoids Clustering Algorithm --// Represents a single cluster, containing its central point (Medoid) and its members. public class Cluster { public int Id { get; private set; } public FoundationInfo Medoid { get; set; } public List<FoundationInfo> Members { get; private set; } public Cluster(int id, FoundationInfo medoid) { Id = id; Medoid = medoid; Members = new List<FoundationInfo>(); } // Calculates the total distance from all members to the medoid. public double CalculateCost() { double cost = 0; foreach (var member in Members) { cost += member.Center.DistanceTo(Medoid.Center); } return cost; } } // Implements the K-Medoids clustering algorithm. public class KMedoids { private readonly List<FoundationInfo> _foundations; private readonly int _k; private readonly int _randomSeed; public List<Cluster> Clusters { get; private set; } public KMedoids(List<FoundationInfo> foundations, int k, int randomSeed) { _foundations = foundations; _k = k; _randomSeed = randomSeed; Clusters = new List<Cluster>(); } public void Run(int maxIterations = 100) { if (_foundations.Count < _k) return; InitializeMedoids(); for (int i = 0; i < maxIterations; i++) { AssignToClusters(); bool changed = UpdateMedoids(); if (!changed) break; // Stop if medoids no longer change. } } // Randomly selects the initial K medoids from the dataset. private void InitializeMedoids() { var random = new Random(_randomSeed); var randomIndices = Enumerable.Range(0, _foundations.Count).OrderBy(x => random.Next()).Take(_k).ToList(); for (int i = 0; i < _k; i++) { Clusters.Add(new Cluster(i, _foundations[randomIndices[i]])); } } // Assigns each foundation to the cluster with the nearest medoid. private void AssignToClusters() { foreach (var c in Clusters) c.Members.Clear();

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Appendix B: C# Script for the Planting Algorithm 1 Script Overview

2 Code Structure

This script operates within the Grasshopper environment and, using Ground data and a Plant database, automatically proposes a planting assemblage suited to the conditions of each target Surface. Environmental variables per Surface, including soil contamination, moisture retention and the presence of bedrock, are evaluated alongside plant tolerances and ecological functions such as stabilisation and detoxification. Based on this evaluation, high-suitability species are filtered, and the proportional mix of three functional guilds—Anchor, Detox (Detoxifier) and Builder—is adjusted dynamically to match local conditions. Each Surface is then partitioned into a 3 × 3 grid, selected species are allocated to cells, and colour and legend data are generated for visualisation.

The code comprises a RunScript method that executes the main logic, supported by lightweight data models and parsing utilities.

RunScript method Execution proceeds in the following order: Data parsing: incoming raw strings are converted into lists of Plant and Ground objects. Environmental suitability filtering: for each Surface, species that meet local constraints are shortlisted. Plant-mix algorithm: guild proportions are determined from Surface attributes, for example slope and contamination, and a species combination is assembled accordingly. Surface subdivision and assignment: each target area is divided into a 3 × 3 grid and the assembled mix is assigned to cells. Outputs: subdivided patches, planting metadata and colour or legend information are emitted in Grasshopper data trees.

Supporting classes and functions Plant class: defines species-level attributes such as Name, Synergy_Group, Strategic_Guild, and environmental tolerances. Ground class: defines site attributes including solar exposure, slope, contamination indicators and bedrock flags. Parsing utilities: ParsePlantData and ParseGroundData convert raw tabular strings into typed objects compatible with Grasshopper workflows.

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/// <summary> /// This class will be instantiated on demand by the Script component. /// </summary> public class Script_Instance : GH_ScriptInstance { #region Utility functions /// <summary>Print a String to the [Out] Parameter of the Script component.</summary> /// <param name=”text”>String to print.</param> private void Print(string text) { /* Implementation hidden. */ } /// <summary>Print a formatted String to the [Out] Parameter of the Script component.</summary> /// <param name=”format”>String format.</param> /// <param name=”args”>Formatting parameters.</param> private void Print(string format, params object[] args) { /* Implementation hidden. */ } /// <summary>Print useful information about an object instance to the [Out] Parameter of the Script component. </summary> /// <param name=”obj”>Object instance to parse.</param> private void Reflect(object obj) { /* Implementation hidden. */ } /// <summary>Print the signatures of all the overloads of a specific method to the [Out] Parameter of the Script component. </summary> /// <param name=”obj”>Object instance to parse.</param> private void Reflect(object obj, string method_name) { /* Implementation hidden. */ } #endregion #region Members /// <summary>Gets the current Rhino document.</summary> private readonly RhinoDoc RhinoDocument; /// <summary>Gets the Grasshopper document that owns this script.</summary> private readonly GH_Document GrasshopperDocument; /// <summary>Gets the Grasshopper script component that owns this script.</summary> private readonly IGH_Component Component; /// <summary> /// Gets the current iteration count. The first call to RunScript() is associated with Iteration==0. /// Any subsequent call within the same solution will increment the Iteration count. /// </summary> private readonly int Iteration; #endregion /// <summary> /// This procedure contains the user code. Input parameters are provided as regular arguments, /// Output parameters as ref arguments. You don’t have to assign output parameters, /// they will have a default value. /// </summary> private void RunScript(List<Brep> Patches, List<double> Scores, List<double> Angles, List<Curve> TerraceAreas, double ZTolerance, int MinGroupSize, int MaxGroupSize, int ClusterCount, double SelectionWeight, int RandomSeed, double AreaPerHousehold, ref object RedZonePatches, ref object YellowZonePatches, ref object RetainingWallGroups, ref object RetainingWallIndices , ref object FoundationGroups, ref object FoundationGroupIndices , ref object ClusteredFoundations, ref object OwnedTerraceAreas, ref object SelectedBuildings, ref object SelectedBuildingIndices, ref object ClusterCenters, ref object Log) { // --- 1. Input Validation and Initialization --// This section checks if the provided inputs are valid and initializes data structures. var log = new List<string>(); log.Add(“=== Input Data Validation ===”); log.Add(string.Format(“Input Patch Count: {0}”, Patches != null ? Patches.Count : 0)); log.Add(string.Format(“Input Score Count: {0}”, Scores != null ? Scores.Count : 0)); log.Add(string.Format(“Input Angle Count: {0}”, Angles != null ? Angles.Count : 0)); log.Add(string.Format(“Input Terrace Area Count: {0}”, TerraceAreas != null ? TerraceAreas.Count : 0)); log.Add(string.Format(“Target Cluster Count: {0}”, ClusterCount)); log.Add(string.Format(“Area per Household: {0}”, AreaPerHousehold)); log.Add(string.Format(“Min Group Size: {0}”, MinGroupSize)); log.Add(string.Format(“Max Group Size: {0}”, MaxGroupSize)); log.Add(string.Format(“Selection Weight (Score={0:P0} / Proximity={1:P0}): “, SelectionWeight, 1 - SelectionWeight)); log.Add(string.Format(“Z-Value Tolerance: {0}”, ZTolerance)); log.Add(string.Format(“Random Seed: {0}”, RandomSeed));

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// Initialize outputs to clear any data from previous runs. RedZonePatches = new List<Brep>(); YellowZonePatches = new List<Brep>();

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for(int i = 0; i < kmedoids.Clusters.Count; i++) { clusterTerraceAreas[i] = 0; ownedTerraces[i] = new List<Brep>(); } if(TerraceAreas != null) { // Convert terrace curves to surfaces (Breps). var validTerraceBreps = new List<Brep>(); foreach(var curve in TerraceAreas) { if(curve == null || !curve.IsClosed) { log.Add(“WARNING: An input terrace curve was not closed and will be ignored.”); continue; } Brep[] breps = Brep.CreatePlanarBreps(curve, Rhino.RhinoDoc.ActiveDoc.ModelAbsoluteTolerance); if(breps != null && breps.Length > 0) { validTerraceBreps.Add(breps[0]); } else { log.Add(“WARNING: A terrace curve could not be converted to a surface and will be ignored.”); } } // Assign each terrace area to the nearest cluster. foreach(var area in validTerraceBreps) { var areaProperties = AreaMassProperties.Compute(area); if(areaProperties == null) continue; Point3d areaCentroid = areaProperties.Centroid; double minDistance = double.MaxValue; int closestClusterId = -1; for(int i = 0; i < kmedoids.Clusters.Count; i++) { var cluster = kmedoids.Clusters[i]; if(cluster.Medoid == null) continue; double dist = areaCentroid.DistanceTo(cluster.Medoid.Center); if(dist < minDistance) { minDistance = dist; closestClusterId = cluster.Id; } } if(closestClusterId != -1) { clusterTerraceAreas[closestClusterId] += areaProperties.Area; ownedTerraces[closestClusterId].Add(area); } } } // --- 6. Finalize Output Data --// This section prepares the clustered and selected data for output. var clusteredFoundationsTree = new DataTree<Brep>(); var selectedBuildingsTree = new DataTree<Brep>(); var selectedBuildingIndicesTree = new DataTree<int>(); var clusterCenterPoints = new List<Point3d>(); var ownedTerracesTree = new DataTree<Brep>();

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// Store the terrace areas owned by this cluster. ownedTerracesTree.AddRange(ownedTerraces[cluster.Id], path); log.Add(string.Format(“ - Cluster {0}: Contains {1} foundation groups. Terrace Area: {2:F0} m^2. Calculated Households: {3}. Selected {4} for building.”, i, cluster.Members.Count, clusterTerraceAreas[cluster.Id], householdsForThisCluster, topFoundations.Count)); } // --- 7. Set Outputs --// Assign all processed data to the component’s output parameters. RedZonePatches = redPatches.Select(p => p.Geometry).ToList(); YellowZonePatches = yellowPatches.Select(p => p.Geometry).ToList(); RetainingWallGroups = ConvertToDataTree(retainingWalls); RetainingWallIndices = ConvertToDataTree(retainingWalls, true); FoundationGroups = ConvertToDataTree(foundations); FoundationGroupIndices = ConvertToDataTree(foundations, true); ClusteredFoundations = clusteredFoundationsTree; SelectedBuildings = selectedBuildingsTree; SelectedBuildingIndices = selectedBuildingIndicesTree; ClusterCenters = clusterCenterPoints; OwnedTerraceAreas = ownedTerracesTree; log.Add(“=== Final Result Summary ===”); log.Add(string.Format(“Red Zone Patches: {0}”, redPatches.Count)); log.Add(string.Format(“Yellow Zone Patches: {0}”, yellowPatches.Count)); log.Add(string.Format(“Retaining Wall Groups: {0}”, retainingWalls.Count)); log.Add(string.Format(“Foundation Groups: {0}”, foundations.Count)); log.Add(string.Format(“Clusters: {0}”, kmedoids.Clusters.Count)); Log = log; } // <Custom additional code> // Defines the classification zones for patches based on their score. public enum ZoneType { Red, Yellow, Green } // A helper class to store all relevant information about a single patch. public class PatchInfo { public int OriginalIndex { get; private set; } public Brep Geometry { get; private set; } public double Score { get; private set; } public double Angle { get; private set; } public ZoneType Zone { get; private set; } public Point3d Center { get; private set; } public PatchInfo(int index, Brep geometry, double score, double angle) { OriginalIndex = index; Geometry = geometry; Score = score; Angle = angle; Zone = ClassifyZone(score); var areaMassProperties = AreaMassProperties.Compute(geometry); Center = areaMassProperties != null ? areaMassProperties.Centroid : Point3d.Unset; } private ZoneType ClassifyZone(double score) { if (score >= 0.6) return ZoneType.Red; if (score >= 0.2) return ZoneType.Yellow; return ZoneType.Green; } } // A helper class to represent a group of patches that form a single foundation. public class FoundationInfo { public int Id { get; private set; } public List<PatchInfo> Patches { get; private set; } public Point3d Center { get; private set; } public double AverageScore { get; private set; } public FoundationInfo(int id, List<PatchInfo> patches) { Id = id; Patches = patches; Point3d center = new Point3d(0, 0, 0); double totalScore = 0; foreach(var patch in patches) { center += patch.Center; totalScore += patch.Score; } Center = center / patches.Count; AverageScore = (patches.Count > 0) ? totalScore / patches.Count : 0; } } 209

// --- Adjacency Analysis and Grouping Algorithms --// Builds a dictionary mapping each patch to a list of its adjacent neighbors. private Dictionary<int, List<int>> BuildAdjacencyList(List<PatchInfo> patches) { var adjacencyList = new Dictionary<int, List<int>>(); double tolerance = 0.001; foreach (var patch in patches) { adjacencyList[patch.OriginalIndex] = new List<int>(); } for (int i = 0; i < patches.Count; i++) { for (int j = i + 1; j < patches.Count; j++) { // Check for physical intersection between two patches. Curve[] intersectionCurves; Point3d[] intersectionPoints; bool intersection = Intersection.BrepBrep(patches[i].Geometry, patches[j].Geometry, tolerance, out intersectionCurves, out intersectionPoints); if (intersection && intersectionCurves.Length > 0) { adjacencyList[patches[i].OriginalIndex].Add(patches[j].OriginalIndex); adjacencyList[patches[j].OriginalIndex].Add(patches[i].OriginalIndex); } } } return adjacencyList; } // Groups patches into connected components using a flood-fill (Breadth-First Search) approach. private List<List<PatchInfo>> GroupAdjacentPatches(List<PatchInfo> patchesToGroup, Dictionary<int, List<int>> adjacencyList, int minGroupSize, int maxGroupSize, double zTolerance) { var allGroups = new List<List<PatchInfo>>(); var assignedPatches = new HashSet<int>(); var patchMap = patchesToGroup.ToDictionary(p => p.OriginalIndex, p => p); // Sort patches to ensure a deterministic grouping order, starting from the same Z-level. var sortedPatches = patchesToGroup.OrderBy(p => Math.Round(p.Center.Z, 3)).ThenByDescending(p => p.Score).ToList(); foreach (var startPatch in sortedPatches) { if (assignedPatches.Contains(startPatch.OriginalIndex)) { continue; } var currentGroup = new List<PatchInfo>(); var queue = new Queue<PatchInfo>(); queue.Enqueue(startPatch); assignedPatches.Add(startPatch.OriginalIndex); while (queue.Count > 0) { var currentPatch = queue.Dequeue(); currentGroup.Add(currentPatch); if (currentGroup.Count >= maxGroupSize) { // If the group reaches max size, add remaining items from queue and stop expanding. while(queue.Count > 0) { currentGroup.Add(queue.Dequeue()); } break; } if (!adjacencyList.ContainsKey(currentPatch.OriginalIndex)) continue; var neighbors = new List<PatchInfo>(); foreach (var neighborIndex in adjacencyList[currentPatch.OriginalIndex]) { if (!assignedPatches.Contains(neighborIndex) && patchMap.ContainsKey(neighborIndex)) { // Only add neighbors that are at a similar Z-height. if(Math.Abs(patchMap[neighborIndex].Center.Z - currentPatch.Center.Z) < zTolerance) { neighbors.Add(patchMap[neighborIndex]); } } } foreach (var neighbor in neighbors) { if (!assignedPatches.Contains(neighbor.OriginalIndex)) { assignedPatches.Add(neighbor.OriginalIndex); queue.Enqueue(neighbor); } } } // Only keep the group if it meets the minimum size requirement. if (currentGroup.Count >= minGroupSize) { if(currentGroup.Count > maxGroupSize) 210

{ allGroups.Add(currentGroup.GetRange(0, maxGroupSize)); } else { allGroups.Add(currentGroup); } } } return allGroups; } // --- K-Medoids Clustering Algorithm --// Represents a single cluster, containing its central point (Medoid) and its members. public class Cluster { public int Id { get; private set; } public FoundationInfo Medoid { get; set; } public List<FoundationInfo> Members { get; private set; } public Cluster(int id, FoundationInfo medoid) { Id = id; Medoid = medoid; Members = new List<FoundationInfo>(); } // Calculates the total distance from all members to the medoid. public double CalculateCost() { double cost = 0; foreach (var member in Members) { cost += member.Center.DistanceTo(Medoid.Center); } return cost; } } // Implements the K-Medoids clustering algorithm. public class KMedoids { private readonly List<FoundationInfo> _foundations; private readonly int _k; private readonly int _randomSeed; public List<Cluster> Clusters { get; private set; } public KMedoids(List<FoundationInfo> foundations, int k, int randomSeed) { _foundations = foundations; _k = k; _randomSeed = randomSeed; Clusters = new List<Cluster>(); } public void Run(int maxIterations = 100) { if (_foundations.Count < _k) return; InitializeMedoids(); for (int i = 0; i < maxIterations; i++) { AssignToClusters(); bool changed = UpdateMedoids(); if (!changed) break; // Stop if medoids no longer change. } } // Randomly selects the initial K medoids from the dataset. private void InitializeMedoids() { var random = new Random(_randomSeed); var randomIndices = Enumerable.Range(0, _foundations.Count).OrderBy(x => random.Next()).Take(_k).ToList(); for (int i = 0; i < _k; i++) { Clusters.Add(new Cluster(i, _foundations[randomIndices[i]])); } } // Assigns each foundation to the cluster with the nearest medoid. private void AssignToClusters() { foreach (var c in Clusters) c.Members.Clear(); foreach (var foundation in _foundations) { Cluster nearestCluster = null; double minDistance = double.MaxValue; foreach (var cluster in Clusters) { if(cluster.Medoid == null) continue; double distance = foundation.Center.DistanceTo(cluster.Medoid.Center); if (distance < minDistance) { minDistance = distance;

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nearestCluster = cluster; } } if(nearestCluster != null) { nearestCluster.Members.Add(foundation); } } } // Tries to improve the clusters by swapping the medoid with a non-medoid member. private bool UpdateMedoids() { bool medoidChanged = false; foreach (var cluster in Clusters) { if (cluster.Members.Count == 0) continue; double minCost = cluster.CalculateCost(); FoundationInfo bestNewMedoid = cluster.Medoid; foreach (var potentialMedoid in cluster.Members) { if (potentialMedoid.Id == cluster.Medoid.Id) continue; var originalMedoid = cluster.Medoid; cluster.Medoid = potentialMedoid; double newCost = cluster.CalculateCost(); if (newCost < minCost) { minCost = newCost; bestNewMedoid = potentialMedoid; medoidChanged = true; } cluster.Medoid = originalMedoid; } cluster.Medoid = bestNewMedoid; } return medoidChanged; } } // --- Utility Functions --// Selects the best foundations from a cluster based on a weighted score of suitability and proximity to the center. private List<FoundationInfo> SelectTopFoundations(Cluster cluster, int count, double weight) { if (cluster.Members.Count == 0 || cluster.Medoid == null) { return new List<FoundationInfo>(); } // Normalize scores and distances to a 0-1 range for fair comparison. double maxScore = cluster.Members.Max(f => f.AverageScore); if (maxScore == 0) maxScore = 1.0; double maxDistance = cluster.Members.Max(f => f.Center.DistanceTo(cluster.Medoid.Center)); if (maxDistance == 0) maxDistance = 1.0; return cluster.Members.OrderByDescending(f => { double normalizedScore = f.AverageScore / maxScore; double distance = f.Center.DistanceTo(cluster.Medoid.Center); // Proximity is the inverse of distance. double normalizedProximity = 1.0 - (distance / maxDistance); // Calculate final priority score using the input weight. return (weight * normalizedScore) + ((1.0 - weight) * normalizedProximity); }).Take(count).ToList(); } // Converts a list of patch groups into a DataTree of Breps for Grasshopper output. private DataTree<Brep> ConvertToDataTree(List<List<PatchInfo>> groups) { var tree = new DataTree<Brep>(); for (int i = 0; i < groups.Count; i++) { var path = new GH_Path(i); tree.AddRange(groups[i].Select(p => p.Geometry), path); } return tree; } // Converts a list of patch groups into a DataTree of Integers (original indices). private DataTree<int> ConvertToDataTree(List<List<PatchInfo>> groups, bool getIndices) { var tree = new DataTree<int>(); for (int i = 0; i < groups.Count; i++) { var path = new GH_Path(i); tree.AddRange(groups[i].Select(p => p.OriginalIndex), path); } return tree; } // </Custom additional code> } 212

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POST-MINING PALIMPSEST Emergent Technologies and Design 2024-2025

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Post-Mining Palimpsest (MSc)