Evaluating Prestressed Concrete Beams with Cracks using Machine Learning

Bridge owners face difficult decisions on whether a bridge should be posted, repaired or replaced when prestressed concrete members have shear related cracks due to overloading. The decisions are currently made based on engineering judgment, costly load-testing or time consuming modeling. Guidance is needed to interpret cracks and their impact on shear capacity to avoid overly conservative load ratings and to keep bridges operational, without compromising safety and economy. This project will develop a tool through machine learning to relate cracking to load history of bridge members. Algorithms will be trained using shear test data from the literature, considering material and geometric properties in addition to crack width as an indicator for distress. The outcome will be the advancement of knowledge on shear evaluation and load rating of in-service precast prestressed concrete bridges with visual signs of distress and guidance for repair actions for bridge owners.

Language

• English

Project

• Status: Active
• Sponsor Organizations:

  University of Illinois, Urbana-Champaign

  Department of Civil and Environmental Engineering
  Newmark Civil Engineering Laboratory
  Urbana, IL  United States  61801-2352

  Office of the Assistant Secretary for Research and Technology

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

  University at Buffalo - SUNY

  221 Ketter Hall
  Buffalo, NY  United States  14260
• Principal Investigators:

  Okumus, Pinar

  Khorasani, Negar Elhami

• Start Date: 20230901
• Expected Completion Date: 20240831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Beams; Cracking; Load factor; Machine learning; Maintenance management; Prestressed concrete bridges; Shear strain
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01893424
• Record Type: Research project
• Source Agency: Transportation Infrastructure Precast Innovation Center
• Files: UTC, RIP
• Created Date: Sep 18 2023 10:04PM