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

卷:

41卷

期数:

2024年03期

页码:

108-119

栏目:

建筑结构

出版日期:

2024-05-20

文章信息/Info

Title:

Structural damage identification based on multi-label convolution neural network

文章编号:

1673-2049(2024)03-0108-12

作者:

秦世强,苏 晟,杨 睿

(武汉理工大学 土木工程与建筑学院,湖北 武汉 430070)

Author(s):

QIN Shiqiang, SU Sheng, YANG Rui

(School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, Hubei, China)

关键词:

结构损伤识别;  卷积神经网络;  多位置损伤;  多类别分类;  多标签分类

Keywords:

structural damage identification;  convolution neural network;  multi-site damage;  multi-class classification;  multi-label classification

分类号:

TU317

DOI:

10.19815/j.jace.2022.07014

文献标志码:

A

摘要:

准确识别结构多位置损伤一直是结构损伤识别的难题。为提升结构多位置损伤识别的准确率,提出一种基于卷积神经网络(CNN)的多标签分类(MLC)方法(CNN-MLC)进行结构损伤识别。该方法将结构多个位置损伤识别转换为多标签分类问题,每个损伤位置均用一个对应的标签表示; 利用CNN强大的特征提取能力,深入挖掘不同损伤工况之间公共损伤位置的相关性,实现结构多位置损伤识别。通过四层框架结构和一座铁路连续梁桥多位置损伤识别验证了CNN-MLC方法的识别准确率,并将其识别结果与基于CNN的多类别分类(MCC)方法(CNN-MCC)和基于示例差异化算法(InsDif)的多标签分类方法(InsDif-MLC)进行了对比。结果表明:框架结构在两位置和三位置损伤工况下,CNN-MLC方法比CNN-MCC方法的识别准确率分别提升2.50%和9.64%,比InsDif-MLC方法识别准确率提升17.50%和29.28%; 对于铁路连续梁桥的两位置损伤和三位置损伤,CNN-MLC方法比CNN-MCC方法识别准确率提升1.63%和6.85%,比InsDif-MLC方法识别准确率提升4.18%和18.49%; 随着损伤位置数量的增加,CNN-MLC方法的识别准确率显著提升。

Abstract:

Accurate identification of structural multi-site damage has always been a difficult problem in structural damage identification. In order to improve the accuracy of structural multi-site damage identification, a multi-label classification method based on convolution neural network(CNN-MLC)was proposed for structural damage identification. In this method, the multi-site damage identification of the structure was transformed into a multi-label classification problem, and each site damage is represented by a separate label. Using the strong feature extraction ability of CNN, the correlation of common damage site between different damage conditions was deeply mined, and the multi-site damage identification was realized. The CNN-MLC method was verified by multi-site damage identification of a four-story frame structure and a railway continuous beam bridge, and the identification results were compared with those of CNN-MCC and InsDif-MLC. The results show that under two-sites and three-sites damage conditions, the recognition accuracy of CNN-MLC is 2.50% and 9.64% higher than that of CNN-MCC, and 17.50% and 29.28% higher than that of InsDif-MLC. For the two-sites damage and three-sites damage of railway continuous beam bridges, the recognition accuracy of CNN-MLC is 1.63% and 6.85% higher than that of CNN-MCC, and 4.18% and 18.49% higher than that of InsDif-MLC. With the increase of the number of damage sites, the recognition accuracy of CNN-MLC is significantly improved.

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相似文献/References:

[1]李书进,赵 源,孔 凡,等.卷积神经网络在结构损伤诊断中的应用[J].建筑科学与工程学报,2020,37(06):29.
　LI Shu-jin,ZHAO Yuan,KONG Fan,et al.Application of Convolutional Neural Network in Structural Damage Identification[J].Journal of Architecture and Civil Engineering,2020,37(03):29.
[2]杨 铄,许清风,王卓琳.基于卷积神经网络的结构损伤识别研究进展[J].建筑科学与工程学报,2022,39(04):38.[doi:10.19815/j.jace.2022.02043]
　YANG Shuo,XU Qing-feng,WANG Zhuo-lin.Research Progress on Structural Damage Detection Based on Convolutional Neural Networks[J].Journal of Architecture and Civil Engineering,2022,39(03):38.[doi:10.19815/j.jace.2022.02043]

备注/Memo

备注/Memo:

收稿日期:2023-07-21
基金项目:国家自然科学基金项目(51608408)
作者简介:秦世强(1987-),男,工学博士,副教授,E-mail:shiqiangqin@whut.edu.cn。

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更新日期/Last Update: 2024-05-20