Prediction of Pavement Performance via Integrated Pavement Health and Traffic Monitoring with Deep Learning and Predictive Modeling

The proposed research will target overcoming the critical limitations in current practice by developing a systematic method for simultaneous pavement performance monitoring and prediction and vehicle and traffic information collection through the integration of pavement dynamic response monitoring, video imaging, traffic flow monitoring, environmental monitoring, numerical pavement simulation with rational mechanics and inverse parameter determination, and an innovative ML architecture.

  • Supplemental Notes:
    • VTI Fed/Match$60000/60000WVU Fed/Match$59818/59818

Language

  • English

Project

  • Status: Active
  • Funding: $239636
  • Contract Numbers:

    69A3551847103

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS)

    Pennsylvania State University
    University Park, PA  United States  16802
  • Project Managers:

    Donnell, Eric

  • Performing Organizations:

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061

    West Virginia University, Morgantown

    Morgantown, WV  United States  26506
  • Principal Investigators:

    Wang, Linbing

    Dai, Fei

  • Start Date: 20220120
  • Expected Completion Date: 20230720
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01835227
  • Record Type: Research project
  • Source Agency: Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS)
  • Contract Numbers: 69A3551847103
  • Files: UTC, RIP
  • Created Date: Feb 2 2022 2:20PM