Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advance Machine Learning Techniques

The objective of this study is to utilize advanced machine learning techniques to develop and validate distress index prediction models for rehabilitation treatments in Louisiana. CatBoosT is a recently developed machine learning algorithm that is widely recognized among the computer science community for its robustness in handling high multi-collinearity and high dimensionality of large datasets. No such algorithm has been employed in previous studies to model the pavement performance. Therefore, this study will investigate the effectiveness of CatBoost in modeling pavement performance in Louisiana. It is expected that the developed models will assist transportation agencies in South-Central U.S enhance their pavement asset management practices and make better economic and defensible decisions on pavement treatment selection.

  • Supplemental Notes:
    • 21PLSU15


  • English


  • Status: Active
  • Funding: $95179
  • 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:

    Transportation Consortium of South-Central States (Tran-SET)

    Louisiana State University
    Baton Rouge, LA  United States  70803
  • Project Managers:

    Mousa, Momen

  • Performing Organizations:

    Louisiana State University, Baton Rouge

    P.O. Box 94245, Capitol Station
    Baton Rouge, LA  United States  70803
  • Principal Investigators:

    Mousa, Momen

  • Start Date: 20210801
  • Expected Completion Date: 20230201
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01833032
  • Record Type: Research project
  • Source Agency: Transportation Consortium of South-Central States (Tran-SET)
  • Contract Numbers: 69A3551747106
  • Files: UTC, RIP
  • Created Date: Jan 20 2022 2:03PM