Improving Deep Learning Models for Bridge Management Using Physics-Based Deep Learning

While various data-driven models are proposed in the literature to forecast bridge deterioration, these models either suffer from low accuracy or are too complex to be applicable in practice. With the research team's prior work, they have demonstrated that deep learning (DL) can significantly outperform other analytical modeling methodologies in bridge deterioration forecasting. However, such models solely rely on data, and unlike physics-based models, cannot benefit from the vast knowledge and experience of bridge engineers encoded in existing physics-based models. As a result, accuracy and efficiency of these models are suboptimal. With this proposal, the team intends to develop hybrid physics-based DL models that can benefit from both effectiveness of DL and the prior knowledge encoded in physics-based bridge models. Such hybrid models are expected to outperform the DL-only models in terms of accuracy and efficiency; hence, enabling further enhanced bridge management.

Language

  • English

Project

  • Status: Active
  • Funding: $120000
  • Contract Numbers:

    69A3551747108

  • 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:

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108
  • Project Managers:

    Tolliver, Denver

  • Performing Organizations:

    Dept. of Civil Engineering

    University of Colorado Denver
    Denver, CO  United States 

    Dept. of Computer Science and Engineering

    University of Colorado Denver
    Denver, CO  United States 
  • Principal Investigators:

    Banaei-Kashani, Farnoush

    Rens, Kevin

  • Start Date: 20210326
  • Expected Completion Date: 20220731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program
  • Source Data: MPC-650

Subject/Index Terms

Filing Info

  • Accession Number: 01769505
  • Record Type: Research project
  • Source Agency: Mountain-Plains Consortium
  • Contract Numbers: 69A3551747108
  • Files: UTC, RiP
  • Created Date: Apr 16 2021 11:51AM