Using Deep Learning for Accurate Detection of Bridge Performance Anomalies

With this project, building on the project team's prior work, their main goal is to introduce improved deep learning based anomaly detection methods for timely and accurate management and monitoring of bridge performance. Such methods can be used to perform predictive analysis of the bridge performance by accurate prediction of quantitative descriptors for the structure deterioration state (e.g., condition ratings) as well as any possible anomalies in the deterioration pattern of the bridge structure. Accurate prediction of these descriptors and anomalies are not only crucial in establishing maintenance priorities and performing proactive bridge monitoring with optimized resource allocation, but also more importantly essential for failure prevention.

• Record URL:
  https://tridurle.wsu.edu/using-deep-learning-for-accurate-detection-of-bridge-performance-anomalies/

Language

• English

Project

• Status: Active
• Funding: $10000
• Contract Numbers:

  69A3551947137

• Sponsor Organizations:

  Transportation Infrastructure Durability & Life Extension

  Washington State University
  Civil & Environmental Engineering
  Pullman, Washington  United States  99164

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Infrastructure Durability & Life Extension

  Washington State University
  Civil & Environmental Engineering
  Pullman, Washington  United States  99164
• Project Managers:

  Kline, Robin

• Performing Organizations:

  University of Colorado Denver

  Denver, Colorado  United States  80204
• Principal Investigators:

  Banaei-Kashani, Farnoush

• Start Date: 20211001
• Expected Completion Date: 20220930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers
• Subprogram: Transportation Infrastructure Durability and Life Extension

Subject/Index Terms

• TRT Terms: Bridge construction; Bridge engineering; Detection and identification systems; Deterioration; Flaw detection; Machine learning; Structural health monitoring
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01773712
• Record Type: Research project
• Source Agency: National Center for Transportation Infrastructure Durability and Life-Extension
• Contract Numbers: 69A3551947137
• Files: UTC, RIP
• Created Date: Jun 5 2021 3:40PM