Using Deep Learning for Accurate Detection of Bridge Performance Anomalies
| 项目名称： | Using Deep Learning for Accurate Detection of Bridge Performance Anomalies |
| 摘要： | With this project, building on the project team's prior work, their main goal is to introduce improved deep learning based anomaly detection methods for timely and accurate management and monitoring of bridge performance. Such methods can be used to perform predictive analysis of the bridge performance by accurate prediction of quantitative descriptors for the structure deterioration state (e.g., condition ratings) as well as any possible anomalies in the deterioration pattern of the bridge structure. Accurate prediction of these descriptors and anomalies are not only crucial in establishing maintenance priorities and performing proactive bridge monitoring with optimized resource allocation, but also more importantly essential for failure prevention. |
| 状态： | Active |
| 资金： | 10000 |
| 资助组织： | Transportation Infrastructure Durability & Life Extension<==>Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Kline, Robin |
| 执行机构： | University of Colorado Denver |
| 开始时间： | 20210630 |
| 预计完成日期： | 20220630 |
| 实际结束时间： | 20220630 |
| 主题领域： | Bridges and other structures；Highways；Maintenance and Preservation |
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