Descriptive and Predictive Deep Learning Analytical Tools for Enhanced Bridge Management: Bridge Subtyping and Bridge Deterioration Forecasting
| 项目名称： | Descriptive and Predictive Deep Learning Analytical Tools for Enhanced Bridge Management: Bridge Subtyping and Bridge Deterioration Forecasting |
| 摘要： | Several U.S. Acts mandate the government to perform regular bridge inspections in order to assure health of the national bridge network. These inspections have been conducted since the 1970s and have generated valuable historic databases of bridge data based in local and state governmental agencies across the nation. While these agencies currently use the inspection data to maintain bridges and prevent failure, the research team believes the inspection databases are heavily underutilized. In particular, with the advent of machine learning and data mining methods, the team envisions data-driven solutions that can derive much more valued hidden knowledge from these databases that can be utilized to significantly enhance bridge management. While in the past various data-driven deterioration models are proposed in the literature to model bridge deterioration, these models either suffer from low accuracy or are too complex to be applicable. Moreover, they only address the problem of deterioration forecasting. Recently deep learning is shown to significantly outperform other analytical modeling methodologies in a variety of application domains. In this study, the team proposes to develop two analytical tools based on deep learning models for enhanced bridge management. The proposed tools will address the problems of bridge subtyping (for descriptive analysis of inspection data to effectively categorize bridges to groups based on their performance characteristics and behavioral trends), and bridge deterioration forecasting (for predictive analysis of the bridge data to accurately identify 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). |
| 状态： | Active |
| 资金： | 80000 |
| 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 Computer Science and Engineering<==>Dept. of Civil Engineering |
| 开始时间： | 20200218 |
| 预计完成日期： | 20220731 |
| 主题领域： | Bridges and other structures；Data and Information Technology；Highways；Maintenance and Preservation；Planning and Forecasting |
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