Quant CR for Transformative Bridge Asset Management

The research team proposes developing an artificial intelligence (AI)-powered quantitative condition rating (QUANT CR) model which operates on a low-cost geographic information system (GIS) platform, aiding local and state bridge owners in maintenance, repair, and replacement (MRR) decisions while preserving the established inspection and condition rating practices. The next generation asset management system leverages the knowledge gained from 50+ years of bridge inspection practices but is predictive, forward-looking, and transformative. QUANT CR embodies insights gained from the understanding of human behavior to better assist bridge owners in decision-making. Thus, the team envisions QUANT CR will be operated in parallel with the existing bridge condition ratings and provide simple decision aids for bridge owners. The team believes bridge condition ratings can be better predicted by modern machine learning methods, leveraging the historic data, evolving element condition ratings, and detailed defect items. Additionally, deep learning widely used for text recognition enables an analysis of inspectors’ narratives describing bridge conditions. Lastly, computer vision and deep generative learning help bridge owners visualize the outcomes of their decisions - MRR actions/inactions, empowering bridge owners.

Language

• English

Project

• Status: Active
• Funding: $202500
• Sponsor Organizations:

  Accelerated Bridge Construction University Transportation Center (ABC-UTC)

  Florida International University
  10555 W. Flagler Street
  Miami, FL  United States  33174

  Office of the Assistant Secretary for Research and Technology

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

  University of Georgia, Athens

  College of Engineering
  Driftmier Engineering Center
  Athens, Georgia  United States  30602
• Start Date: 20240102
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Asset management; Bridge management systems; Computer vision; Condition surveys; Machine learning
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01924838
• Record Type: Research project
• Source Agency: Accelerated Bridge Construction University Transportation Center (ABC-UTC)
• Files: UTC, RIP
• Created Date: Jul 21 2024 3:03PM