Smart Structures and Systems

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Cointegration based modeling and anomaly detection approaches using monitoring data of a suspension bridge

  • Ziyuan Fan (School of Transportation, Southeast University) ;
  • Qiao Huang (School of Transportation, Southeast University) ;
  • Yuan Ren (School of Transportation, Southeast University) ;
  • Qiaowei Ye (School of Transportation, Southeast University) ;
  • Weijie Chang (Zhejiang Zhoushan Sea-Crossing Bridge Co., Ltd.) ;
  • Yichao Wang (School of Transportation, Southeast University)
  • Received : 2020.12.01
  • Accepted : 2022.04.17
  • Published : 2023.02.25
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Abstract

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.

Keywords

Acknowledgement

The research was supported by Scientific Research Project of CICO (No. ZC20210604XBFW000200/01), the Academician Special Science Research Project of CCCC (No. YSZX-03-2020-01-B, and YSZX-03-2021-02-B), the Jiangsu Transportation Science and Technology Project (No. 2020Y19-(1)) and the Fundamental Research Funds for the Central Universities (No. 2242022R10077).

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