Study on hybrid model combining super learner and physic-based models for SHM in bridges using low-cost BWIM

Many structural health monitoring (SHM) techniques have been devised over the past decades. However, there is no one-size-fits-all solution that can be applied to all bridges for structural assessments. Bridge-based weight-in-motion systems (BWIM) use the structure’s response to estimate vehicles’ load distribution. This technology is primarily used to obtain vehicle axle weights efficiently in public. BWIM can be a candidate that overcomes the shortfall of SHM. The use of BWIM systems for SHM has rarely been investigated. The objectives are (1) to study and deploy low-cost BWIM sensors for accurate SHM, (2) to evaluate the S-BWIM system, and (3) assessment of the hybrid model capacity combining physics-based mathematical models (PSM) and practical machine-learning (ML) models. A new low-cost BWIM system verified with numerical results will be installed in the local area, Dallas, and Fort Worth (DFW). This study will help Region 6 communities, where low-cost measurements are already used, and prediction models are publicly available for monitoring both traffic loads and bridge conditions. The development of a hybrid model generally adaptable for various conditions of bridges will be a major contribution to the research community. A comparative study of the proposed machine learning algorithm (super learner) with low-cost BWIM sensors will also be implemented in Region 6.

• Record URL:
  https://transet.lsu.edu/research-in-progress/
• Supplemental Notes:
  • 20STUTA26

Language

• English

Project

• Status: Completed
• Funding: $ 122000
• Contract Numbers:

  69A3551747106

• 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:

  Transportation Consortium of South-Central States (Tran-SET)

  Louisiana State University
  Baton Rouge, LA  United States  70803
• Project Managers:

  Mousa, Momen

• Performing Organizations:

  University of Texas at Arlington

  Department of Civil Engineering
  Box 19308
  Arlington, TX  United States  76019
• Principal Investigators:

  Ham, Suyun

• Start Date: 20200801
• Expected Completion Date: 20220201
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Bridge engineering; Bridges; Machine learning; Mathematical models; Sensors; Structural health monitoring; Traffic; Weigh in motion
• Geographic Terms: Dallas (Texas); Fort Worth (Texas)
• Subject Areas: Bridges and other structures; Data and Information Technology; Design; Highways; Maintenance and Preservation; Materials;

Filing Info

• Accession Number: 01757534
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States (Tran-SET)
• Contract Numbers: 69A3551747106
• Files: UTC, RIP
• Created Date: Nov 10 2020 8:35PM