A Multi-Label Classification Method for Anomaly Detection of Bridge Structural Health Monitoring Data
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Bibliographic Details
| Author(s): |
Zhiqiang Shang
(Shandong Hi-Speed Group Innovation Research Institute, Shandong Key Laboratory of Highway Technology and Safety Assessment, Jinan, Shandong, China)
Gongfeng Xin (Shandong Hi-Speed Group Innovation Research Institute, Shandong Key Laboratory of Highway Technology and Safety Assessment, Jinan, Shandong, China) Ye Xia (Tongji University, Shanghai, China) Limin Sun 
(Tongji University, State Key Lab for Disaster Reduction in Civil Engineering, Qizhi Institute, Shanghai, China)
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022 | ||||
| Published in: | IABSE Congress Nanjing 2022 | ||||
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| Page(s): | 1280-1287 | ||||
| Total no. of pages: | 8 | ||||
| DOI: | 10.2749/nanjing.2022.1280 | ||||
| Abstract: |
In past years, massive data has been accumulated by many bridge structural health monitoring systems, and various methods have been proposed to detect data anomalies to ensure the reliability of subsequent data analysis. However, these methods are incapable of determining if there still exist usable data segments in a data sequence providing a specified anomaly type has been identified. To address the problem, a deep learning-based multi-label classification method is proposed in this paper. A multi-label anomaly dataset is first constructed using monitored acceleration data of a cable-stayed bridge. Then, a multilabel anomaly classification model based on a convolutional neural network is developed and trained with the constructed dataset. The developed method exhibits desirable performance in simultaneously detecting the existence of both usable data and the other data anomalies. |
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| Keywords: |
bridge acceleration structural health monitoring deep learning data anomaly multilabel classification
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| Copyright: | © 2022 International Association for Bridge and Structural Engineering (IABSE) | ||||
| License: | This creative work is copyrighted material and may not be used without explicit approval by the author and/or copyright owner. |
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