Graduation Semester and Year
Fall 2025
Document Type
Thesis
Degree Name
Master of Science in Civil Engineering
Department
Civil Engineering
First Advisor
Karthikeyan Loganathan
Abstract
Efficient pavement management is essential for keeping university parking lots in good condition. At the University of Texas at Arlington, parking areas experience heavy use and environmental wear, which can cause damage if not addressed early. Traditional methods for assessing pavement conditions are time-consuming, interrupt daily activities, and are expensive. Hence, there is a need for better solutions. This study introduces a drone-based pavement management approach that uses the Pavement Condition Index (PCI) system. The PCI is a numerical scale from 0 to 100 that rates pavement quality based on surface condition. A high PCI score (85-100) indicates the pavement is in good condition and requires little maintenance, while a low score (below 55) indicates the pavement is in poor condition and requires major repairs. This rating helps asset managers prioritize maintenance and allocate resources efficiently.
Unmanned Aerial Vehicles (UAVs) were deployed with standardized flight parameters to collect high-resolution imagery of the University of Texas at Arlington (UTA) parking lots. The imagery was processed in Pix4D using uniform protocols and markers to delineate parking areas. A hybrid assessment combining UAV imagery and manual field verification was used to compute PCI values for designated sections and to create condition maps. Analysis of UTA parking facilities reveals a wide range of pavement conditions, with PCI scores from Poor to Very Good.
Parking lots graded Poor displayed severe deterioration—cracking, raveling, and potholes. In contrast, lots rated Good to Very Good exhibited minimal wear, illustrating the effectiveness of previous maintenance. The range of PCI ratings across lots highlights the varied pavement performance on campus and stresses the need for targeted maintenance. The results show that some areas require immediate rehabilitation, while others are suitable for cost-effective preventive treatments. This approach enables more efficient allocation of maintenance resources at the university.
Future research will extend this study by incorporating deep learning-based automated distress detection using UAV. This approach aims to reduce reliance on manual verification and enhance the scalability of the PCI assessments across the network. Advanced computer vision models will be trained on extensive pavement imagery datasets to automatically identify, classify, and quantify distress types with high accuracy.
Keywords
Pavement Condition Index, Parking Lots, UAVs, Pavement Management System, ASTM D6433, Pavement Distress
Disciplines
Civil and Environmental Engineering | Civil Engineering | Construction Engineering and Management | Transportation Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Pasuluru, Omesh, "DEVELOPMENT OF A DRONE-ASSISTED PAVEMENT MANAGEMENT FRAMEWORK BASED ON THE PAVEMENT CONDITION INDEX FOR UNIVERSITY PARKING LOTS" (2025). Civil Engineering Theses. 465.
https://mavmatrix.uta.edu/civilengineering_theses/465
Included in
Civil Engineering Commons, Construction Engineering and Management Commons, Transportation Engineering Commons