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Smart Structures and Systems Volume 30, Number 6, December 2022 , pages 557-569 DOI: https://doi.org/10.12989/sss.2022.30.6.557 |
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Vibration-based structural health monitoring using CAE-aided unsupervised deep learning |
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Minte Zhang, Tong Guo, Ruizhao Zhu, Yueran Zong and Zhihong Pan
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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. | ||
| Key Words | ||
| damage identification; on-site test; structural health monitoring; unsupervised deep learning; vibration assessment | ||
| Address | ||
| (1) Minte Zhang, Tong Guo, Ruizhao Zhu, Yueran Zong: School of Civil Engineering, Southeast University, Nanjing, Jiangsu, People's Republic of China; (2) Zhihong Pan: School of Architecture and Civil Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, People's Republic of China; (3) Tong Guo: The Centre for BIM Studies, Smart City and Sustainable Development Academy, Chongqing, China. | ||