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Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform
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DOI：10.1007/s10489-020-02092-6.png)
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Abstract
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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.
Keywords:
Civil structures
Structural damage detection
Continuous wavelet transform
Convolutional neural networks
Sensor data
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