Smart Structures and Systems

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Operational performance evaluation of bridges using autoencoder neural network and clustering

  • Huachen Jiang (Shanghai Key Laboratory of Engineering Structure Safety, SRIBS) ;
  • Liyu Xie (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Da Fang (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education) ;
  • Chunfeng Wan (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education) ;
  • Shuai Gao (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education) ;
  • Kang Yang (School of Railway Transportation, Shanghai Institute of Technology) ;
  • Youliang Ding (Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education) ;
  • Songtao Xue (Department of Disaster Mitigation for Structures, Tongji University)
  • Received : 2021.12.29
  • Accepted : 2024.01.25
  • Published : 2024.03.25
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Abstract

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

Keywords

Acknowledgement

This work is supported by the National Key Research and Development Program of China (2021YFE0112200), Shanghai Municipal Transportation Commission (JT2023-KY-003), Key Research Support Project of SRIBS (KY10000038.20230065), the Japan Society for Promotion of Science (Kakenhi No. 18K04438), the Tohoku Institute of Technology research Grant, and the Housing & UrbanRural Construction Commission of Shanghai Municipality (2023-002-029).

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