International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
EVALUATION OF PAVEMENT SURFACE DISTRESS USING SMARTPHONE SENSOR DATA AND IMAGE-BASED ANALYSIS TECHNIQUES Jadeja Krushanrajsinh Ramsinh1, Dr.Prachi Pandya 2, Durgesh Kumar Singh 3 , Dr.C.G.Patel 4 1 Final Year Student, Department of Civil Engineering (IoT) Ganpat University, Gujarat, India 2 Assistant Professor, IOT, Department of Civil Engineering Ganpat University, Gujarat, India
3 Assistant Professor, UVPCE, Department of Civil Engineering Ganpat University, Gujarat, India 4 Professor & Head, UVPCE, Department of Civil Engineering Ganpat University, Gujarat, India
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Abstract - Pavement surface distress evaluation is a
The results indicate that poor-condition bituminous pavements exhibit significantly higher acceleration variations compared to good-condition pavements, reflecting severe surface deterioration. Cement concrete pavements showed moderate average acceleration values with distinct sharp peaks corresponding to rigid pavement joints. The strong agreement between smartphone sensor data and image-based distress identification demonstrates the effectiveness of the proposed approach. The study concludes that smartphone-based sensing combined with image-based analysis provides a practical, low-cost, and reliable method for preliminary pavement surface distress evaluation, suitable for academic research and resourceconstrained pavement monitoring applications.
critical component of road maintenance and pavement management systems, as surface deterioration directly affects ride quality, vehicle operating costs, and road user safety. Conventional pavement condition assessment methods such as manual visual inspections, inertial profilers, and specialized pavement inspection vehicles, although effective, are often expensive, time-consuming, and require trained personnel. These limitations restrict their frequent application, particularly for urban local roads and low-volume road networks in developing regions. With recent advancements in smartphone technology, embedded sensors and high-resolution cameras offer a promising lowcost alternative for pavement condition monitoring.
Key Words: Pavement distress, Smartphone sensors, Image-based analysis, Road condition assessment, Lowcost monitoring
This study presents a detailed evaluation of pavement surface distress using smartphone sensor data combined with image-based analysis techniques. Extensive field data were collected using an iPhone 16 Pro Max mounted inside a moving vehicle. Accelerometer sensor data and pavement surface images were recorded simultaneously while traversing selected urban road sections in Gandhinagar, Gujarat, India (PIN: 382421). The study area included four representative road sections, consisting of three bituminous pavements categorized as good, moderate, and poor based on surface condition, and one cement concrete pavement section exhibiting joint-related surface irregularities. To ensure consistency and reliability, all data were collected using the same smartphone, identical mounting position, and a controlled vehicle speed range of approximately 45– 55 km/h.
1. INTRODUCTION Road transportation infrastructure plays a vital role in economic development, social connectivity, and regional mobility. The performance and serviceability of road networks largely depend on pavement surface conditions, which are continuously subjected to traffic loading, environmental effects, and material aging. Over time, these factors cause surface distresses such as cracking, potholes, surface disintegration, joint distress, and increased roughness. If not identified early, these distresses accelerate deterioration, reduce ride quality, compromise safety, and increase maintenance and rehabilitation costs.
The collected accelerometer data were analyzed using basic statistical and graphical methods to evaluate relative variations in pavement surface condition. Acceleration magnitude was considered as an indicator of pavementinduced vehicle vibration. In parallel, a large number of pavement surface images were examined through visual inspection to identify and classify common surface distresses such as cracking, potholes, surface disintegration, and joint distress. The sensor-based results were validated through image-based observations to establish a qualitative correlation between vibration response and visible pavement defects.
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Impact Factor value: 8.315
Pavement surface condition evaluation is a key component of pavement management systems. Traditional assessment methods include manual visual surveys and specialized equipment such as inertial profilers, laser scanning systems, and automated inspection vehicles. Although reliable, these methods are often expensive, operationally complex, and require trained personnel, making frequent large-scale monitoring difficult, especially for urban and low-volume roads in developing regions. In this context, the present study evaluates pavement surface distress using smartphone sensor data combined
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