Abnormal data detection for structural health monitoring: State-of-the-art review
Structural health monitoring (SHM) is widely used to monitor and assess the condition and performance of engineering structures such as, buildings, bridges, dams, and tunnels. Owing to sensor defects, data acquisition errors, and environmental interference, abnormal data are often collected and stor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Developments in the Built Environment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666165924000188 |
| _version_ | 1797267120198778880 |
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| author | Yang Deng Yingjie Zhao Hanwen Ju Ting-Hua Yi Aiqun Li |
| author_facet | Yang Deng Yingjie Zhao Hanwen Ju Ting-Hua Yi Aiqun Li |
| author_sort | Yang Deng |
| collection | DOAJ |
| description | Structural health monitoring (SHM) is widely used to monitor and assess the condition and performance of engineering structures such as, buildings, bridges, dams, and tunnels. Owing to sensor defects, data acquisition errors, and environmental interference, abnormal data are often collected and stored in monitoring systems. The abnormal data in this study are essentially different from so-called “abnormal state data,” which result from structural physical damage or performance degradation. Abnormal data are totally related to the external interference rather than changes in the inherent structural features. However, abnormal data can significantly affect the performance assessment of engineering structures. It is imperative to detect and remove abnormal data from measurements to avoid misjudging structural performance in SHM. This paper summarizes abnormal data detection in the SHM field and discusses relevant challenges. Moreover, background knowledge regarding abnormal data detection is introduced. Abnormal data detection methods are then classified into statistical probability methods, predictive models, and computer vision methods. The advantages, disadvantages, and scope of each method are investigated. An example of detecting abnormal monitoring data for a cable-stayed bridge is introduced. In addition, the issues of existing studies are summarized, and future study interests are discussed. |
| first_indexed | 2024-03-08T11:53:18Z |
| format | Article |
| id | doaj.art-71460e0600fe47239e436e26e921b92d |
| institution | Directory Open Access Journal |
| issn | 2666-1659 |
| language | English |
| last_indexed | 2024-04-25T01:11:32Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developments in the Built Environment |
| spelling | doaj.art-71460e0600fe47239e436e26e921b92d2024-03-10T05:13:00ZengElsevierDevelopments in the Built Environment2666-16592024-03-0117100337Abnormal data detection for structural health monitoring: State-of-the-art reviewYang Deng0Yingjie Zhao1Hanwen Ju2Ting-Hua Yi3Aiqun Li4School of Civil Engineering and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China; International Joint Laboratory of Safety and Energy Conservation for Ancient Buildings, Ministry of Education, Beijing, ChinaSchool of Civil Engineering and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Civil Engineering and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China; Corresponding author. School of Civil Engineering and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.School of Civil Engineering and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China; International Joint Laboratory of Safety and Energy Conservation for Ancient Buildings, Ministry of Education, Beijing, China; School of Civil Engineering, Dalian University of Technology, Dalian, ChinaSchool of Civil Engineering and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China; International Joint Laboratory of Safety and Energy Conservation for Ancient Buildings, Ministry of Education, Beijing, ChinaStructural health monitoring (SHM) is widely used to monitor and assess the condition and performance of engineering structures such as, buildings, bridges, dams, and tunnels. Owing to sensor defects, data acquisition errors, and environmental interference, abnormal data are often collected and stored in monitoring systems. The abnormal data in this study are essentially different from so-called “abnormal state data,” which result from structural physical damage or performance degradation. Abnormal data are totally related to the external interference rather than changes in the inherent structural features. However, abnormal data can significantly affect the performance assessment of engineering structures. It is imperative to detect and remove abnormal data from measurements to avoid misjudging structural performance in SHM. This paper summarizes abnormal data detection in the SHM field and discusses relevant challenges. Moreover, background knowledge regarding abnormal data detection is introduced. Abnormal data detection methods are then classified into statistical probability methods, predictive models, and computer vision methods. The advantages, disadvantages, and scope of each method are investigated. An example of detecting abnormal monitoring data for a cable-stayed bridge is introduced. In addition, the issues of existing studies are summarized, and future study interests are discussed.http://www.sciencedirect.com/science/article/pii/S2666165924000188Structural health monitoringAbnormal data detectionStatistical probability methodPredictive modelComputer vision |
| spellingShingle | Yang Deng Yingjie Zhao Hanwen Ju Ting-Hua Yi Aiqun Li Abnormal data detection for structural health monitoring: State-of-the-art review Developments in the Built Environment Structural health monitoring Abnormal data detection Statistical probability method Predictive model Computer vision |
| title | Abnormal data detection for structural health monitoring: State-of-the-art review |
| title_full | Abnormal data detection for structural health monitoring: State-of-the-art review |
| title_fullStr | Abnormal data detection for structural health monitoring: State-of-the-art review |
| title_full_unstemmed | Abnormal data detection for structural health monitoring: State-of-the-art review |
| title_short | Abnormal data detection for structural health monitoring: State-of-the-art review |
| title_sort | abnormal data detection for structural health monitoring state of the art review |
| topic | Structural health monitoring Abnormal data detection Statistical probability method Predictive model Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2666165924000188 |
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