International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
LIFE CYCLE COST ANALYSIS OF NATM USING ANN Mayur Jadhav1, Dr. Madhulika Sinha2 1ME Student, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani University of
Mumbai.
2 Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani University of Mumbai.
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ABSTRACT
The New Austrian Tunneling Method (NATM) is a widely adopted tunneling technique known for its flexibility and costeffectiveness in various geotechnical conditions. However, the complexity of NATM construction processes and the variability of geological factors make accurate cost estimation challenging. This study focuses on conducting a comprehensive Life Cycle Cost Analysis (LCCA) of NATM projects by integrating Artificial Neural Networks (ANN) as a predictive modeling tool. LCCA evaluates the total cost of a project over its entire lifespan, including initial construction, maintenance, and operation, providing a holistic economic assessment. ANN, with its ability to model complex nonlinear relationships and learn from historical data, is utilized to predict the life cycle costs more accurately than traditional methods. The research involves collecting extensive data from completed NATM tunneling projects, including geological parameters, construction variables, and cost elements. This data is used to train and validate the ANN model, enabling it to predict cost outcomes under varying conditions effectively. The results demonstrate that ANN can significantly enhance the precision of cost forecasting, facilitating better decision-making and budget planning. Moreover, the integration of LCCA with ANN supports stakeholders in optimizing resource allocation and mitigating financial risks throughout the tunnel's operational life. This study contributes to the field by offering an innovative approach that combines geotechnical engineering with advanced machine learning techniques to improve economic evaluations of complex tunneling projects, promoting sustainable infrastructure development.
1. INTRODUCTION This study explores the integration of Artificial Neural Networks (ANN) into the Life Cycle Cost (LCC) analysis of the New Austrian Tunneling Method (NATM), a widely adopted approach in tunnel construction due to its flexibility and adaptability across diverse geological settings. The success of NATM hinges on meticulous planning and effective cost control, making LCC analysis essential. LCC analysis assesses all expenditures associated with a project over its entire lifespan, from design and construction to operation and maintenance[1]. It enables the identification of long-term cost-efficient strategies by accounting for initial investments, future repairs, and operational expenses. In tunnel construction, such comprehensive evaluation helps stakeholders in choosing the most economically viable solutions without compromising safety or project requirements[2]. Traditional methods of LCC analysis, however, often fall short in capturing the complexity and uncertainties inherent in tunneling projects. To overcome this, the study incorporates Artificial Neural Networks as a powerful computational method to enhance prediction accuracy and decision-making capabilities. ANNs are well-suited to process vast historical data, detect complex patterns, and forecast future costs based on various influencing parameters like geological conditions, construction practices, and material choices. Their ability to adapt and learn enables a more realistic and responsive cost model[3]. By employing ANN in LCC analysis for NATM, this research aims to develop a smart, data-driven framework for cost estimation and optimization[4]. This approach not only improves accuracy but also empowers engineers and project managers to minimize financial risks, optimize resources, and ensure adherence to safety standards. The synergy between ANN and traditional cost analysis marks a significant step forward in modernizing the financial planning and management of tunneling infrastructure.
2. LITERATURE REVIEW Recent developments in Life Cycle Cost Analysis (LCCA) across various construction and infrastructure domains reflect a growing emphasis on combining traditional cost estimation methods with advanced computational technologies. For instance underline the importance of Environmental Product Declarations (EPDs) as foundational to sustainable building assessment, with Life Cycle Assessment (LCA) playing a central role[5]. Their global study revealed 27 key challenges in implementing LCA for EPDs, categorized into seven groups, including data quality, methodological limitations, and technological constraints. The
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