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Smart Structures and Systems Volume 33, Number 3, March 2024 , pages 189-199 DOI: https://doi.org/10.12989/sss.2024.33.3.189 |
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Operational performance evaluation of bridges using autoencoder neural network and clustering |
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Huachen Jiang, Liyu Xie, Da Fang, Chunfeng Wan, Shuai Gao, Kang Yang, Youliang Ding and Songtao Xue
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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 vehicleinduced 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. | ||
| Key Words | ||
| autoencoder; early warning; Gaussian mixture model; strain measurement; structural health monitoring; strain measurement | ||
| Address | ||
| (1) Huachen Jiang: Shanghai Key Laboratory of Engineering Structure Safety, SRIBS, Shanghai 200032, China; (2) Liyu Xie, Songtao Xue: Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China; (3) Da Fang, Chunfeng Wan, Shuai Gao, Youliang Ding: Southeast University, Key Laboratory of Concrete and Prestressed Concrete Structure of Ministry of Education, Nanjing 210096, China; (4) Kang Yang: School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, PR China; (5) Songtao Xue: Department of Architecture, Tohoku Institute of Technology, Sendai, Miyagi 982-8577, Japan. | ||