Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel
Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further red...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/6185 |
| _version_ | 1797408067408625664 |
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| author | Jianzhong Chen Xinghong Jiang Yu Yan Qing Lang Hui Wang Qing Ai |
| author_facet | Jianzhong Chen Xinghong Jiang Yu Yan Qing Lang Hui Wang Qing Ai |
| author_sort | Jianzhong Chen |
| collection | DOAJ |
| description | Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further reduce the burden of manual analysis. Thus, this study proposed a dynamic warning method for SHM data based on ARIMA and applied it to the concrete strain data of the Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel. First, wavelet threshold denoising was applied to filter noise from the SHM data. Then, the feasibility and accuracy of establishing an ARIMA model were verified, and it was adopted to predict future time series of SHM data. After that, an anomaly detection scheme was proposed based on the dynamic model and dynamic threshold value, which set the confidence interval of detected anomalies based on the statistical characteristics of the historical series. Finally, a hierarchical warning system was defined to classify anomalies according to their detection threshold and enable hierarchical treatments. The illustrative example of the HZMB immersed tunnel verified that a three-level (5.5 σ, 6.5 σ, and 7.5 σ) dynamic warning schematic can give good results of anomalies detection and greatly improves the efficiency of SHM data management of the tunnel. |
| first_indexed | 2024-03-09T03:51:59Z |
| format | Article |
| id | doaj.art-e8e1bb9cc2f046cebd561984f6677def |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:51:59Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e8e1bb9cc2f046cebd561984f6677def2023-12-03T14:27:02ZengMDPI AGSensors1424-82202022-08-012216618510.3390/s22166185Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed TunnelJianzhong Chen0Xinghong Jiang1Yu Yan2Qing Lang3Hui Wang4Qing Ai5College of Civil Engineering, Chongqing University, Chongqing 400044, ChinaChina Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd., Chongqing 400067, ChinaHong Kong-Zhuhai-Macao Bridge Authority, Zhuhai 519060, ChinaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaStructural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further reduce the burden of manual analysis. Thus, this study proposed a dynamic warning method for SHM data based on ARIMA and applied it to the concrete strain data of the Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel. First, wavelet threshold denoising was applied to filter noise from the SHM data. Then, the feasibility and accuracy of establishing an ARIMA model were verified, and it was adopted to predict future time series of SHM data. After that, an anomaly detection scheme was proposed based on the dynamic model and dynamic threshold value, which set the confidence interval of detected anomalies based on the statistical characteristics of the historical series. Finally, a hierarchical warning system was defined to classify anomalies according to their detection threshold and enable hierarchical treatments. The illustrative example of the HZMB immersed tunnel verified that a three-level (5.5 σ, 6.5 σ, and 7.5 σ) dynamic warning schematic can give good results of anomalies detection and greatly improves the efficiency of SHM data management of the tunnel.https://www.mdpi.com/1424-8220/22/16/6185dynamic warning methodstructural health monitoringARIMAHong Kong–Zhuhai–Macao Bridgeimmersed tunnel |
| spellingShingle | Jianzhong Chen Xinghong Jiang Yu Yan Qing Lang Hui Wang Qing Ai Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel Sensors dynamic warning method structural health monitoring ARIMA Hong Kong–Zhuhai–Macao Bridge immersed tunnel |
| title | Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel |
| title_full | Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel |
| title_fullStr | Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel |
| title_full_unstemmed | Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel |
| title_short | Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel |
| title_sort | dynamic warning method for structural health monitoring data based on arima case study of hong kong zhuhai macao bridge immersed tunnel |
| topic | dynamic warning method structural health monitoring ARIMA Hong Kong–Zhuhai–Macao Bridge immersed tunnel |
| url | https://www.mdpi.com/1424-8220/22/16/6185 |
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