Rapid Damage Assessment in Infrastructure Systems using Vibration Measurements within a Machine Learning Framework

The primary goal of this proposal is to develop different Machine Learning (ML) algorithms for the rapid identification of damage in bridge structures using the bridge’s dynamic response during regular service operation. These algorithms are in theory applicable to any dynamical system but will be tailored specifically for bridge structures. This research will provide diagnostic tools that can be directly used in real time by bridge owners for rapid damage assessment. By collecting data and analyzing them in near real time, the algorithms should be able to provide information on the conditions of the structure and this could help in (1) controlling the traffic operation on the bridge as well as (2) in prioritizing resources in terms of rehabilitation/maintenance operations. The intended outcome of the project is the development of Machine Learning based tools for rapid damage assessment in bridges which are expected to have tremendous implications in practice and education of modern civil engineers.


  • English


  • Status: Active
  • Funding: $240000
  • 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 Advanced Infrastructure and Transportation

    Rutgers University
    100 Brett Road
    Piscataway, NJ  United States  08854-8058
  • Project Managers:

    Szary, Patrick

  • Performing Organizations:

    Columbia University

    610 SW Mudd
    500W 120th Street
    New York, New York  United States  10027
  • Principal Investigators:

    Betti, Raimondo

    Brugger, Adrian

  • Start Date: 20220901
  • Expected Completion Date: 20230831
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01867738
  • Record Type: Research project
  • Source Agency: Center for Advanced Infrastructure and Transportation
  • Contract Numbers: 69A3551847102, CAIT-UTC-REG74
  • Files: UTC, RIP
  • Created Date: Dec 16 2022 12:23PM