Post-disaster Damage Assessment of Bridge Systems

Leveraging recent advances in artificial intelligence, a novel signal processing technique will be developed to build a surrogate model for an accurate prediction of engineering demand parameters of interest (e.g., peak column drift ratio). The epistemic uncertainty of the surrogate model will be quantified and integrated with other uncertainties in performance-based engineering methodology so that rapid condition assessment and loss estimation can be provided in a probabilistic manner. The proposed framework will be first verified through the data generated using finite element analyses, and its reliability will be validated through proof-of-concept experiments. The intended outcome of the project is the development of a sensor-based framework for reliable post-disaster damage assessment of bridge systems. This technology is expected to automate the process of damage detection, and to assess and identify risks associated with each bridge immediately after a natural disaster. Practical guidelines to implement the methodology will be prepared, with input from the industry collaborators.

Language

• English

Project

• Status: Active
• Funding: $62317
• Contract Numbers:

  CAIT-UTC-REG50

  69A3551847102

• 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:

  HDR

  500 Seventh Avenue
  New York, NY  United States  10018
• Project Managers:

  LaTuso, Christopher

  Szary, Patrick

• Performing Organizations:

  University of Buffalo

  ,    
• Principal Investigators:

  Liang, Xiao

• Start Date: 20210201
• Expected Completion Date: 20220131
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bridge engineering; Disaster resilience; Disasters and emergency operations; Finite element method; Risk analysis; Sensors; Structural health monitoring
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01776219
• Record Type: Research project
• Source Agency: Center for Advanced Infrastructure and Transportation
• Contract Numbers: CAIT-UTC-REG50, 69A3551847102
• Files: UTC, RIP
• Created Date: Jul 7 2021 2:56PM