AI/video-driven SHM and Lifespan Estimation of In-service Bridges

Pedestrian and vehicular bridges are essential components of the transportation infrastructure. They undergo various challenges, including aging effects, contamination, unexpected service excitations, extreme environmental conditions, and accidental loads. Comprehensive structural evaluations ensure bridges' safety and adaptability to meet contemporary service performance standards. The Departments of Transportation continuously seek near-real-time, accurate data about the health and status of these bridges to facilitate informed decisions about their maintenance and viability. A current challenge is the lack of comprehensive automated systems and tools continuously evaluating bridge health. This research aims to fill this gap by establishing a continuous monitoring system for two select bridges in the District of Columbia, harnessing Artificial Intelligence (AI). Methodology: The study's methodology leverages AI-powered Structural Health Monitoring (SHM), focusing on advanced computer vision techniques. Employing video cameras and advanced video analysis methods to extract information on structural behaviors. Work in Year 1: This three-year project commences with the primary focus during the first year on establishing data collection and monitoring logistics (including selecting bridges for monitoring), instrument installation, and outlining video tracking algorithms. Furthermore, the year will be dedicated to data accumulation to develop a finite element model for pre-training and methods evaluation. Post the initial instrumentation; the succeeding project years will revolve around continuous monitoring of the chosen bridges, focusing on extracting the inherent dynamic properties in these structures and implementing analysis techniques to extract information on their structural behaviors.


    • English


    • Status: Active
    • Contract Numbers:


    • Sponsor Organizations:

      Center for Durable and Resilient Transportation Infrastructure

      University of Texas, Arlington
      Arlington, TX  United States  76019

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Start Date: 20230901
    • Expected Completion Date: 0
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01909622
    • Record Type: Research project
    • Source Agency: Center for Durable and Resilient Transportation Infrastructure
    • Contract Numbers: 69A3552348339
    • Files: UTC, RIP
    • Created Date: Feb 23 2024 4:17PM