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Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning
Author(s) -
Lin Yizhou,
Nie Zhenhua,
Ma Hongwei
Publication year - 2017
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12313
Subject(s) - computer science , artificial intelligence , convolutional neural network , feature extraction , noise (video) , pattern recognition (psychology) , deep learning , wavelet , feature (linguistics) , network packet , detector , energy (signal processing) , visualization , set (abstract data type) , image (mathematics) , computer network , telecommunications , philosophy , linguistics , statistics , mathematics , programming language
Structural damage detection is still a challenging problem owing to the difficulty of extracting damage‐sensitive and noise‐robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low‐level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise‐free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.