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2022年 第16卷 第1期

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《结构与土木工程前沿（英文）》 >> 2022年 第16卷 第1期 doi: 10.1007/s11709-021-0777-x

Digital image correlation-based structural state detection through deep learning

1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;2. Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou 510405, China;1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;3. Guangzhou Municipal Engineering Testing Co., Ltd., Guangzhou 510520, China

收稿日期： 2021-05-27 录用日期： 2022-01-04 发布日期： 2022-01-15
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This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

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