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Digital image correlation-based structural state detection through deep learning

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Abstract

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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Authors and Affiliations

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Shuai Teng, Gongfa Chen, Shaodi Wang, Jiqiao Zhang & Xiaoli Sun

  2. Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou, 510405, China

    Shaodi Wang

  3. Guangzhou Municipal Engineering Testing Co., Ltd., Guangzhou, 510520, China

    Xiaoli Sun

Authors
  1. Shuai Teng
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  2. Gongfa Chen
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  3. Shaodi Wang
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  4. Jiqiao Zhang
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  5. Xiaoli Sun
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Correspondence to Gongfa Chen.

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Cite this article

Teng, S., Chen, G., Wang, S. et al. Digital image correlation-based structural state detection through deep learning. Front. Struct. Civ. Eng. 16, 45–56 (2022). https://doi.org/10.1007/s11709-021-0777-x

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  • DOI: https://doi.org/10.1007/s11709-021-0777-x

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