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. 2022 Aug 18;22(16):6185.
doi: 10.3390/s22166185.

Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong-Zhuhai-Macao Bridge Immersed Tunnel

Jianzhong Chen  1   2 , Xinghong Jiang  2   3 , Yu Yan  4 , Qing Lang  5 , Hui Wang  5 , Qing Ai  5
Affiliations

Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong-Zhuhai-Macao Bridge Immersed Tunnel

Jianzhong Chen et al. Sensors (Basel). .

Abstract

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.

Keywords: ARIMA; Hong Kong–Zhuhai–Macao Bridge; dynamic warning method; immersed tunnel; structural health monitoring.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Longitudinal layout of the HZMB immersed tunnel.
Figure 2
Figure 2
Cross-sectional geometry of the HZMB immersed tunnel.
Figure 3
Figure 3
SHM system of the HZMB.
Figure 4
Figure 4
Time series before and after denoising.
Figure 5
Figure 5
Point anomaly.
Figure 6
Figure 6
Contextual anomaly.
Figure 7
Figure 7
Collective anomaly: (a) Collective anomaly (a global view); (b) Before the anomaly; (c) The anomaly; (d) After the anomaly.
Figure 8
Figure 8
Timing diagram of concrete strain data on 2 June 2020.
Figure 9
Figure 9
Static ARIMA result.
Figure 10
Figure 10
Model checking plots: (a) Timing graph of the residual; (b) Distribution plot of the residual; (c) Q-Q plot of the residual; (d) ACF of the residual.
Figure 11
Figure 11
ARIMA prediction at different time periods: (a) Forecast results at 0:00; (b) Forecast results at 4:00; (c) Forecast results at 8:00; (d) Forecast results at 12:00; (e) Forecast results at 16:00; (f) Forecast results at 20:00.
Figure 12
Figure 12
Dynamic ARIMA error sequence.
Figure 13
Figure 13
Flow chart of the anomaly detection process.
Figure 14
Figure 14
Anomaly proportion under different std. coefficients.
Figure 15
Figure 15
Anomalies of different levels.
See this image and copyright information in PMC

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