Deep convolutional transfer learning-based structural damage detection with domain adaptation

Most data-driven structural damage detection methods are built upon the assumption that enough labeled data is available and both training and test data have the same underlying distribution, which limit their successful applications in practical engineering. To solve the problem, a novel structural damage detection method is proposed by using deep convolutional transfer learning. In the method, one-dimensional deep convolutional neural network and two-dimensional deep convolutional neural network are combined to mine more fine-grained features with spatiotemporal characteristic from raw vibration data. And, a novel domain adaptation technology, combining multikernel maximum mean discrepancy and local maximum mean discrepancy, is developed to align the distribution of global domains and relevant subdomains among different domains, which could mine more fine-grained features for each category and improve the transfer performance. Transfer experiments on two different structures are implemented to verify the effectiveness of the proposed method. Furthermore, a new solution is found by taking advantage of the damage knowledge learnt from other structure to implement damage detection when very small damage samples are available. The results show that the proposed method achieved superior detection performance over the existing popular methods.