Improving Deep Learning Models for Bridge Management Using Physics-Based Deep Learning
| 项目名称： | Improving Deep Learning Models for Bridge Management Using Physics-Based Deep Learning |
| 摘要： | While various data-driven models are proposed in the literature to forecast bridge deterioration, these models either suffer from low accuracy or are too complex to be applicable in practice. With the research team's prior work, they have demonstrated that deep learning (DL) can significantly outperform other analytical modeling methodologies in bridge deterioration forecasting. However, such models solely rely on data, and unlike physics-based models, cannot benefit from the vast knowledge and experience of bridge engineers encoded in existing physics-based models. As a result, accuracy and efficiency of these models are suboptimal. With this proposal, the team intends to develop hybrid physics-based DL models that can benefit from both effectiveness of DL and the prior knowledge encoded in physics-based bridge models. Such hybrid models are expected to outperform the DL-only models in terms of accuracy and efficiency; hence, enabling further enhanced bridge management. |
| 状态： | Active |
| 资金： | 120000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Tolliver, Denver |
| 执行机构： | Dept. of Civil Engineering<==>Dept. of Computer Science and Engineering |
| 开始时间： | 20210326 |
| 预计完成日期： | 20220731 |
| 主题领域： | Bridges and other structures；Highways；Maintenance and Preservation；Planning and Forecasting |
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