Application Research on Deep Convolutional Neural Network Considering Residual Learning in Structural Damage Identification

LI Shujin; XIONG Shuqi; FAN Peiran; WANG Gang

doi:10.13204/j.gyjzg21101009

Volume 52 Issue 7

Oct. 2022

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LI Shujin, XIONG Shuqi, FAN Peiran, WANG Gang. Application Research on Deep Convolutional Neural Network Considering Residual Learning in Structural Damage Identification[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(7): 192-198. doi: 10.13204/j.gyjzg21101009

Citation:

LI Shujin, XIONG Shuqi, FAN Peiran, WANG Gang. Application Research on Deep Convolutional Neural Network Considering Residual Learning in Structural Damage Identification[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(7): 192-198. doi: 10.13204/j.gyjzg21101009

Citation:

LI Shujin, XIONG Shuqi, FAN Peiran, WANG Gang. Application Research on Deep Convolutional Neural Network Considering Residual Learning in Structural Damage Identification[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(7): 192-198. doi: 10.13204/j.gyjzg21101009

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Application Research on Deep Convolutional Neural Network Considering Residual Learning in Structural Damage Identification

doi: 10.13204/j.gyjzg21101009

1. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China;
2. Central Research Institute of Building and Construction(Shenzhen) Co., Ltd., Shenzhen 518054, China

Received Date: 2021-10-10
Available Online: 2022-10-28

Abstract

Abstract

A deep convolutional neural network damage identification method considering residual learning was proposed and applied to the damage identification of the frame structure joints. The proposed method was deeply discussed by means of experimental research, and the results showed that this method could solve the problems of convergence difficulty and poor recognition accuracy caused by the network degradation and gradient explosion, dispersion problems when the network deepening. In the comparative study of joint damage identification of test frame, the convergence speed and accuracy of deep convolutional neural network considering residual learning were higher than those of shallow conventional neural network and deep neural network, and had high accuracy and stability, and increased the possibilities to build a deeper and more complex network for damage diagnosis of complex structures in engineering. In addition, in order to improve the quality and quantity of training samples for network, a new data processing method was proposed according to the law of sample division. This method could significantly increase the sample size for training, weaken the information loss caused by data truncation under the same conditions, and greatly improve the recognition accuracy and convergence speed, and the research showed its effectiveness and applicability.
- damage identification,
- convolutional neural network,
- experimental study,
- engineering structure,
- residual learning

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References(21)

References

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