Bridge monitoring through a hybrid approach leveraging a modal updating technique and an artificial intelligence (AI) method

An early damage identification process in bridge structures may offer an opportunity to slowdown progressive failure and thus prevent catastrophic collapses. With a structural health monitoring system which allows real-time measurement of structural responses, this may be possible if proper techniques are employed to identify early damage in bridge structures. In doing so, the proposed project will integrate two methods (i.e., a model updating technique and an artificial intelligence (AI) prediction) that can compensate for each other’s the weakness that otherwise imposed difficulty in precise real-time application of health monitoring systems. This project will leverage a mode-updating technique with high-fidelity experimental data to obtain an accurate digital model that represents an actual bridge model. The drawback of the model updating technique (i.e., high computational time) will be overcome by applying an artificial intelligence algorithm such as artificial neural networks that are known to be computationally efficient while perusing high accuracy. The proposed approach will then result in a fast and accurate method (i.e., a model-based data-driven method) for early damage identification of bridge structures.

    Language

    • English

    Project

    • Status: Active
    • Funding: $26,650.00
    • Sponsor Organizations:

      Department of Transportation

      Federal Motor Carrier Safety Administration
      1200 New Jersey Avenue, SE
      Washington, DC    20590
    • Managing Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626
    • Project Managers:

      Brinkerhoff, Cort

    • Performing Organizations:

      University of Hawaii, Manoa

      2540 Dole Street
      Honolulu, HI  United States  96822
    • Principal Investigators:

      Cho, Chunhee

    • Start Date: 20210816
    • Expected Completion Date: 20220815
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01775749
    • Record Type: Research project
    • Source Agency: METRANS Transportation Center
    • Files: RIP
    • Created Date: Jun 30 2021 12:02PM