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Full text release has been delayed at the author's request until April 25, 2026

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LEVERAGING SENSOR DATA AND MACHINE LEARNING ALGORITHMS TO ENHANCE PAVEMENT MANAGEMENT PRACTICES

Talha, Sk Abu
http://orcid.org/0000-0003-1880-9804
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712917705344806

Abstract Details

2024, PhD, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
This research aims at optimizing the winter pavement management practices by (1) developing an efficient yet simple pothole surveillance system utilizing state-of-the-art connected and autonomous vehicle (CAV) sensors, including LiDAR and camera and (2) developing prediction models with machine learning algorithms and applying survival analysis to estimate the service life of commonly used pothole patching methods/materials combinations and ultimately calculating the life cycle cost associated with it. For pothole detection, automobile industry-grade mechanical LiDAR and a camera were used. Two pothole detection approaches were proposed. In the first approach, data collected from the camera and LiDAR were fused and consequently applied deep learning-based object detection algorithm to locate potholes and extract point cloud data corresponding to the potholes using the coordinates of the detected bounding box. The second approach detected potholes directly from the cross-sectional LiDAR point cloud data using a deep learning-based object detection technique followed by utilizing the point clouds' spatial information to estimate the potholes' dimensions. In both approaches, the timestamps of the GPS and LiDAR were synchronized to locate the pothole coordinate. While the first approach showed great promise in estimating the pothole dimensions with unprecedented accuracy, the validation of the second approach demonstrated that the approach can be reliably employed to locate potholes and estimate the dimensions at different highway speeds. Two life cycle cost-based approaches were proposed to optimize pothole patching practice with various combinations of patching materials and methods considering different factors such as traffic volume, size of the potholes, and climatic conditions. The first approach included a non-parametric survival approach to identify the factors affecting the survival life of the pothole patches, consequently using this information to estimate the survival life of patches using parametric survival analysis. In the second approach, different machine-learning models were employed to predict the performance of the patch. The best-performing model was then used to develop a performance curve to estimate the survival life at any given performance level. Finally, the estimated survival life was used to calculate the life cycle cost of different patching methods/materials combinations. Considering the magnitude of winter pothole maintenance initiatives, the suggested framework provides transportation agencies with the opportunity to achieve substantial cost savings. By selecting optimized pothole patching methods and materials based on specific circumstances, agencies can make informed decisions that maximize efficiency and resource utilization, thereby improving overall program effectiveness.
Munir Nazzal, Ph.D. (Committee Chair)
Matthew Steiner, Ph.D. (Committee Member)
Lei Wang, Ph.D. (Committee Member)
Nabil Nassif (Committee Member)
171 p.
Civil Engineering
CAV; LiDAR; Life Cycle Cost; Pothole; Deep Learning

Recommended Citations

Citations

  • Talha, S. A. (2024). LEVERAGING SENSOR DATA AND MACHINE LEARNING ALGORITHMS TO ENHANCE PAVEMENT MANAGEMENT PRACTICES [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712917705344806

    APA Style (7th edition)

  • Talha, Sk Abu. LEVERAGING SENSOR DATA AND MACHINE LEARNING ALGORITHMS TO ENHANCE PAVEMENT MANAGEMENT PRACTICES. 2024. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712917705344806.

    MLA Style (8th edition)

  • Talha, Sk Abu. "LEVERAGING SENSOR DATA AND MACHINE LEARNING ALGORITHMS TO ENHANCE PAVEMENT MANAGEMENT PRACTICES." Doctoral dissertation, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1712917705344806

    Chicago Manual of Style (17th edition)

ucin1712917705344806
© 2024, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.