Smart Structures and Systems

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Vibration-based structural health monitoring using CAE-aided unsupervised deep learning

  • Minte, Zhang (School of Civil Engineering, Southeast University) ;
  • Tong, Guo (School of Civil Engineering, Southeast University) ;
  • Ruizhao, Zhu (School of Civil Engineering, Southeast University) ;
  • Yueran, Zong (School of Civil Engineering, Southeast University) ;
  • Zhihong, Pan (School of Architecture and Civil Engineering, Jiangsu University of Science and Technology)
  • Received : 2022.04.10
  • Accepted : 2022.09.08
  • Published : 2022.12.25
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Abstract

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

Keywords

Acknowledgement

The authors gratefully acknowledge the Ministry of Science and Technology of the People's Republic of China (No.2018YFE0206100). The research was financially supported by the Jiangsu Provincial Department of Science and Technology under (No.BE2019107).

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