Use of Machine Learning to Predict Long-Term Skid Resistant of Concrete Pavements

This project intends to use laboratory-measured concrete frictional properties data and build a machine learning (ML) model to predict the friction performance of concrete pavements. The model uses aggregate mineralogy, concrete mixture proportions, and concrete surface finish (tined vs. ground and grooved) as input parameters. It predicts the surface friction (measured via the dynamic friction test, DFT, ASTM E 1911; Komaragiri et al., 2020) and surface texture (measured via the circular track meter, CTM, ASTM E 2157; Komaragiri et al., 2020) of concrete pavements undergoing accelerated polishing tests as a function of number of polishing cycles as well as the terminal friction and texture values associated with long-term polishing. The model further uses these outputs to predict the international friction index (IFI) parameters, including the friction number (F60) and slip speed (SP), as well as the equivalent skid number (SN) measured by locked-wheel trailer test (ASTM E524) at a given speed and for a given concrete pavement.


    • English


    • Status: Active
    • Funding: $48,546
    • Contract Numbers:


    • 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

      Rajabipour, Farshad

    • Performing Organizations:

      Pennsylvania State University, University Park

      Thomas D. Larson Pennsylvania Transportation Institute
      Research Office Building
      University Park, PA  United States  16802-4710
    • Principal Investigators:

      Rajabipour, Farshad

    • Start Date: 20200511
    • Expected Completion Date: 20210211
    • Actual Completion Date: 20211130
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01731108
    • 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 18 2020 11:35AM