Synthesis of Information Related to Highway Practices. Topic 54-14. Artificial Intelligence Applications for Automated Pavement Condition Evaluation

3D laser-based pavement imaging systems have been widely adopted by highway agencies in the last decade for automated pavement condition assessment while 2D imaging technologies and smart phones are also used to perform their pavement condition evaluation, especially for local transportation agencies. These collected pavement images are then used to extract pavement distresses semi- or fully-automatically through various methods. Among these methods, models based on Artificial Intelligence (AI) with Machine Learning and Deep Learning (ML/DL) have gained much attention for pavement distress identification in the last several years. However, most AI models either are not yet fully integrated with how highway agencies use the pavement distress data or have not been sufficiently developed to rely on quality 3D pavement image data. The collected distresses such as cracking, faulting, flushing, and raveling are key indicators for triggering pavement maintenance and rehabilitation activities. Without clearly understanding the ultimate use of this distress data by state agencies, the AI model development efforts for distress detection and/or classification, which includes AI model formulation, distress annotation, training, and performance evaluation, could be misguided, thus failing to reach their full potential. For example, the AI-based models for automated crack detection using the classification of image blocks having cracking distress may not be able to output accurate cracking length and width information. Therefore, the produced model outcome may not meet the state agencies' need for project-level applications such as planning crack sealing projects. On the other hand, the performance of supervised-learning AI models for automated pavement distress extraction heavily relies on several factors including the quality of the pavement image data used, data size and diversity, the annotation quality (labeled ground truth distresses), the model formulation, model training, etc. However, the performance evaluation method used for many developed models is not always clear, especially for the diversity of the data used for that evaluation and its established ground truth. This ambiguity makes the comparison of the performance of different models challenging and unreliable. The objective of this synthesis is to document state DOT current practices of automated pavement distress identification and AI (ML/DL) technologies for pavement condition evaluation. Information to be gathered includes (but is not limited to): (1) Requirements for automated pavement distress identification; (2) Various applications of pavement distress condition information; (3) Types of agency decision-making supported by pavement condition data; (4) Artificial intelligence (e.g., machine learning, deep learning) technologies, tools, and models currently being applied to pavement distress detection and classification and to pavement condition evaluation; and (5) Ground truth / reference / benchmark data used in AI-technique development, training, and evaluation.

• Record URL:
  http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=5297

Language

• English

Project

• Status: Active
• Funding: $55000
• Contract Numbers:

  Project 20-05, Topic 54-14

• Sponsor Organizations:

  National Cooperative Highway Research Program

  Transportation Research Board
  500 Fifth Street, NW
  Washington, DC  United States  20001

  American Association of State Highway and Transportation Officials (AASHTO)

  444 North Capitol Street, NW
  Washington, DC  United States  20001

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Project Managers:

  Harrigan, Edward

• Performing Organizations:

  Nichols Consulting Engineers

  1885 S. Arlington Avenue
  Suite 111
  Reno, NV  United States  89509
• Principal Investigators:

  Pierce, Linda

• Start Date: 20230504
• Expected Completion Date: 20241104
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Detection and identification technologies; Pavement distress; State departments of transportation; State of the practice
• Subject Areas: Administration and Management; Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01845351
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: Project 20-05, Topic 54-14
• Files: TRB, RIP
• Created Date: May 17 2022 10:21AM