Machine Learning Maintenance Cost Forecast for Better Infrastructure LCCA

Traditional life cycle cost analysis (LCCA) involves considering multiple design/project alternatives to deliver the same outcome, and then comparing the net present value (NPV) of each alternative using estimated costs. An issue with LCCA/NPV analysis is that one must assume that once a project alternative has been chosen, the cash flows are set, and the alternative is irreversible. Using financial technology (FinTech) tools, specifically data analysis, along with remote-sensing data for structural health monitoring (SHM), it is possible to develop an intelligent forecasting tool that can better estimate infrastructure maintenance costs in real-time. This project aims to develop and test a Machine Learning (ML)/FinTech-based forecasting tool that can be used to provide an intelligent forecast of maintenance costs, allowing for a more accurate and actionable LCCA based on real-time data.


    • English


    • Status: Active
    • Contract Numbers:


    • Sponsor Organizations:

      Center for Durable and Resilient Transportation Infrastructure

      University of Texas, Arlington
      Arlington, TX  United States  76019

      Office of the Assistant Secretary for Research and Technology

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

      Missouri University of Science & Technology, Rolla

      Department of Engineering
      202 University Center
      Rolla, MO    65409
    • Start Date: 20230901
    • Expected Completion Date: 0
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01909698
    • Record Type: Research project
    • Source Agency: Center for Durable and Resilient Transportation Infrastructure
    • Contract Numbers: 69A3552348339
    • Files: UTC, RIP
    • Created Date: Feb 23 2024 4:35PM