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Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform

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Abstract

Structural damage detection is of very importance to improve reliability and safety of civil structures. A novel sensor data-driven structural damage detection method is proposed in this paper by combining continuous wavelet transform (CWT) with deep convolutional neural network (DCNN). In this method, time-frequency images are obtained by CWT from original one-dimensional sensor signals. And, DCNN is designed to mine structural damage features from the time-frequency images and distinguish different structural damage condition. The proposed method is carried out on three-story building structure dataset and steel frame dataset. The experimental results show that the proposed method has the high accuracy and robustness of the damage detection compared with other existing machine learning methods.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China under the Grant No.51875225 and the 18th batch of graduate student innovation fund of Huazhong University of Science and Technology No. 2020yjsCXCY059.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Zuoyi Chen & Chao Deng

  2. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yanzhi Wang, Jun Wu & Kui Hu

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  1. Zuoyi Chen
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  2. Yanzhi Wang
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  3. Jun Wu
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  4. Chao Deng
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  5. Kui Hu
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Correspondence to Jun Wu.

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Chen, Z., Wang, Y., Wu, J. et al. Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform. Appl Intell 51, 5598–5609 (2021). https://doi.org/10.1007/s10489-020-02092-6

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  • DOI: https://doi.org/10.1007/s10489-020-02092-6

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