Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data

Many research groups in the structural health monitoring community have made efforts to utilize deep learning-based approaches for damage detection on a variety of structures. Among these approaches, structural damage detection through deep convolutional neural networks using raw structural response data has received great attention. However, structural responses are affected not only by structural properties but also by excitation characteristics. For detecting of structures' damage under seismic excitations, different seismic excitations definitely cause varied structural responses data. In practice, it is impossible to accurately predict the characteristics of future seismic excitation for pre-training the deep convolutional neural network. Therefore, it is essential to investigate the autonomous detection of structural element damage subject to unknown seismic excitation. In this article, a new approach is proposed for detecting structural damage subject to unknown seismic excitation based on a convolutional neural network with wavelet-based transmissibility of structural response data. The transmissibility functions of structural response data are used to eliminate the influence of different seismic excitations. Moreover, contrary to the traditional Fourier transform in the conventional transmissibility function, wavelet-based transmissibility function is presented using the ability in subtle information acquisition of wavelet transform. The wavelet-based transmissibility data of structural responses are used as the inputs to constructed deep convolutional neural networks. Both a numerical simulation example and an experimental test are used to validate the performance of the proposed approach based on deep convolutional neural network.