|Table of Contents|

Application of Convolutional Neural Network in Structural Damage Identification(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2020年06期
Page:
29-37
Research Field:
Publishing date:

Info

Title:
Application of Convolutional Neural Network in Structural Damage Identification
Author(s):
LI Shu-jin1;  ZHAO Yuan1;  KONG Fan1;  ZHANG Yuan-jin2
1. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, Hubei, China; 2. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, Hubei, China
Keywords:
damage identification;  convolutional neural network;  deep learning;  frame structure;  wavelet transform
PACS:
TU312.3
DOI:
-
Abstract:
The application of convolutional neural network in damage identification of engineering structure was discussed in depth. Taking the identification of damage location of multi-layer frame structure nodes as the research object, a one-dimensional convolutional neural network model based on original signal and Fourier frequency domain information and a two-dimensional convolutional neural network model based on wavelet transform data were constructed, which could learn directly from the structural dynamic response signals and complete the classification diagnosis. The type of input data sample, training time, prediction accuracy, shallow and deep convolutional neural network and the influence of different damage degree on damage identification were studied. The results show that the convolutional neural network can effectively extract the damage features from the dynamic response information of the structure, and has a high recognition accuracy. Compared with using acceleration response sample directly, using frequency domain data after Fourier transform as training samples can make the CNN convergence faster and more stable, and the performance of deep CNN is better than that of shallow CNN. The convolutional neural network can be used in the damage diagnosis of engineering structures, especially in the big data processing and the ability to solve complex problems, and has great advantages and wide application prospects compared with the other traditional diagnosis methods.

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