Development of deep learning based automated data collection technology for coastal highway pavements

The harsh environmental conditions of coastal areas, including extreme weather events, saltwater corrosion, tides, winds, and waves, make maintaining these roads a significant challenge. One solution to improve the resilience of coastal roads is through improved pavement maintenance, which involves maintaining roads at the right time to extend their service life cost-effectively. However, accurately monitoring the conditions of coastal roads is a challenge. Therefore, the research problem is to identify and evaluate available technologies for pavement condition evaluation in coastal areas, and assess their efficiency, accuracy, and cost-effectiveness. The research aims to provide a solution to the challenges faced in evaluating the performance of coastal roads which can support a maintenance program specific to coastal roads. The research will improve the resilience of coastal roads, reduce repair costs, and promote the economic growth of coastal areas. Automated pavement condition data collection using image processing is currently the most widely used technology for monitoring pavement conditions. Over 33 states in the U.S. use this technology to assess network-level pavement conditions. However, the accuracy of these technologies is significantly impacted by various environmental factors when using traditional image processing methods. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), there is an opportunity to improve automated pavement condition data collection technologies, enabling accurate and efficient detection of pavement conditions. Thus, the objective of this research is to use AI/ML enhanced automated pavement condition data collection technologies to monitor coastal road pavements. This will be achieved by developing an algorithm specifically tailored to the environmental characteristics of coastal areas. To accomplish this research objective, the following specific goals will be met: (1) To establish a library of pavement surface images from coastal areas, representing various combinations of pavement distress types and severity levels under different coastal environmental conditions. (2) To develop AI/ML models and tools for coastal pavement condition assessment based on pavement surface images. The AI/ML models will have sufficiently high accuracy for detecting distresses under coastal environments. (3) To test and validate the developed AI/ML models on actual pavement case studies representing coastal environmental conditions

Language

• English

Project

• Status: Active
• Funding: $179650
• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Texas Department of Transportation

  125 E. 11th Street
  Austin, TX  United States  78701-2483
• Managing Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Coastal Research and Education Actions for Transportation Equity

  Texas State University
  San Marcos, TX  United States  77666
• Project Managers:

  Bruner, Britain

  Kulesza, Stacey

• Performing Organizations:

  Coastal Research and Education Actions for Transportation Equity

  Texas State University
  San Marcos, TX  United States  77666
• Principal Investigators:

  Wang, Feng

• Start Date: 20230901
• Expected Completion Date: 20250228
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Algorithms; Artificial intelligence; Automatic data collection systems; Coasts; Machine learning; Pavement maintenance
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01895142
• Record Type: Research project
• Source Agency: Coastal Research and Education Actions for Transportation Equity
• Files: UTC, RIP
• Created Date: Oct 2 2023 8:49PM