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:
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Supplemental Notes:
- 20STUTA26
Language
- English
Project
- Status: Completed
- Funding: $ 122000
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Contract Numbers:
69A3551747106
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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
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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