27 March 2018 Automated damage-sensitive feature extraction using unsupervised convolutional neural networks
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Many convolutional neural networks (CNN) –based approaches were proposed and applied to detect damage in various civil structures in recent years. Usually, the training process of the classical CNN requires a large number of labeled data which is from the monitored structure in undamaged and various damaged scenarios. However, it is impractical to acquire sufficient data that can be exactly labeled with damaged from the infrastructures in service as training data. Thus, we propose a novel unsupervised CNN-based approach to automatically extract optimal feature representations from the unlabeled data in a single class. In the case study, a known dataset from an undamaged scenario is used to train CNN and a dataset from an unknown scenario is used to test the trained CNN. The proposed approach in unsupervised learning is capable of extracting feature representations from the raw acceleration signals that are sensitive to the presence of damage. Then, the extracted damage-sensitive features are fed into a one-class support vector machine (OC-SVM) for novelty detection. The feature set from the undamaged dataset is taken as training dataset to train the OC-SVM, and the extracted features from the unknown dataset are used for testing. In order to verify the effectiveness of the proposed approach in structural damage localization, a number of accelerometers are used to acquire sufficient raw acceleration data from a lab-scale steel bridge, and the preliminary experimental results show that the proposed novel CNN-based approach performs very well in damage localization.
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Zilong Wang, Zilong Wang, Young-jin Cha, Young-jin Cha, } "Automated damage-sensitive feature extraction using unsupervised convolutional neural networks", Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981J (27 March 2018); doi: 10.1117/12.2295966; https://doi.org/10.1117/12.2295966

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