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.

• Record URL:
  https://www.mountain-plains.org/research/details.php?id=547

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: 20240731
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: MPC-650

Subject/Index Terms

• TRT Terms: Bridge management systems; Deterioration; Machine learning; Physics
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; Planning and Forecasting;

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 7:38PM