Pavement Distress Evaluation and Cracking Indices Generation using Deep Learning
The North Carolina Department of Transportation (NCDOT) manages the nation's second-largest roadway network. To ensure safety and efficiency of this network, it is crucial to implement timely and effective maintenance strategies. This research project aims to address these needs. In this research, to help optimize maintenance strategies, non-crack distresses will be classified, segmented, and quantified using cutting-edge deep learning techniques. Since an on-going research project has already completed similar tasks for varying types of cracks, upon completion of this proposed study, all types (crack and non-crack) of distresses across the 14 Divisions monitored by NCDOT can be classified and quantified using deep learning models. With this approach, it is estimated that a comprehensive state-wide pavement performance assessment can be completed in one week. Consequently, the outcomes of this proposed study, combined with those from the on-going research, will enable timely updates of distress indices and Pavement Condition Rating (PCR) values. This enhanced responsiveness of NCDOT’s PMS will significantly benefit North Carolina’s roadway network in terms of durability and sustainability. In addition, specific crack metric and index, namely the Pavement Surface Cracking Metric (PSCM) and the Pavement Surface Cracking Index (PSCI), will be calculated using the ASTM E3303-21 standard. The calculated results will be highly accurate, as the length of every crack is quantified at a pixel level. Moreover, this task will standardize and enhance the reliability of crack assessments, contributing to a more effective PMS managed by NCDOT. One potential challenge that the UNC Charlotte researchers face is identifying certain types of uncommon non-crack distresses from the raw images provided by NCDOT. The lack of training data for these distresses can directly impact the performance of the corresponding deep learning models. To address this issue, the researchers plan to work closely with NCDOT engineers to pinpoint the locations of these distresses and gather sufficient distress data for model training purposes. Another potential challenge is the time-consuming nature of the image annotation process, a common obstacle in studies utilizing deep learning techniques for image processing. Building on the experience gained from the on-going study, the researchers plan to evaluate both AI-based and self-supervised learning approaches to expedite the annotation process effectively. It should be noted that transferred learning from deep learning models developed in the on-going NCDOT research project will be used to develop new models in this study. This approach allows resources spent on one task to be transferred, reused, and adapted for other related tasks, significantly reducing the computational resources and time, and more importantly, leading to improved performance of newly developed models. In summary, this research project is proposed to improve maintenance efficiency, reduce repair costs, and support NCDOT’s sustainability goals. Various approaches will be utilized to ensure the success of this project. The methods and tools developed in this project can be applied to address other challenges in the future.
Language
- English
Project
- Status: Active
- Funding: $392,220.00
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Contract Numbers:
RP2026-15
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Sponsor Organizations:
North Carolina Department of Transportation
Research and Development
1549 Mail Service Center
Raleigh, NC United States 27699-1549 -
Managing Organizations:
North Carolina Department of Transportation
Research and Development
1549 Mail Service Center
Raleigh, NC United States 27699-1549 -
Project Managers:
Kadibhai, Mustansir
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Performing Organizations:
University of North Carolina - Charlotte
9201 University City Blvd
Charlotte, North Carolina United States 28223-0001 -
Principal Investigators:
Chen, Don
- Start Date: 20250801
- Expected Completion Date: 20270731
- Actual Completion Date: 0
- USDOT Program: Pavements and Materials
Subject/Index Terms
- TRT Terms: Classification; Deep learning; Image analysis; Metrics (Quantitative assessment); Pavement cracking; Pavement distress; Pavement management systems
- Identifier Terms: North Carolina Department of Transportation
- Subject Areas: Highways; Maintenance and Preservation; Pavements;
Filing Info
- Accession Number: 01957965
- Record Type: Research project
- Source Agency: North Carolina Department of Transportation
- Contract Numbers: RP2026-15
- Files: RIP, STATEDOT
- Created Date: Jun 13 2025 12:48PM