Study on hybrid model combining super learner and physic-based models for SHM in bridges using low-cost BWIM
| 项目名称： | Study on hybrid model combining super learner and physic-based models for SHM in bridges using low-cost BWIM |
| 摘要： | 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. |
| 状态： | Active |
| 资金： | 122000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Mousa, Momen |
| 执行机构： | University of Texas at Arlington |
| 开始时间： | 20200801 |
| 预计完成日期： | 20220201 |
| 主题领域： | Bridges and other structures；Data and Information Technology；Design；Highways；Maintenance and Preservation；Materials |
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