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

卷:

42卷

期数:

2025年01期

页码:

101-111

栏目:

建筑结构

出版日期:

2025-01-20

文章信息/Info

Title:

Damage diagnosis of complex structure based on convolution neural network and multi-label classification

文章编号:

1673-2049(2025)01-0101-11

作者:

李书进¹,杨繁繁¹,张远进²

(1. 武汉理工大学 土木工程与建筑学院,湖北 武汉 430070; 2. 武汉理工大学 安全科学与应急管理学院,湖北 武汉 430070)

Author(s):

LI Shujin¹, YANG Fanfan¹, ZHANG Yuanjin²

(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)

关键词:

损伤诊断;  卷积神经网络;  多标签分类;  框架结构;  深度学习

Keywords:

damage diagnosis;  convolution neural network;  multi-label classification;  frame structure;  deep learning

分类号:

TU312.3

DOI:

10.19815/j.jace.2023.02027

文献标志码:

A

摘要:

为研究复杂空间框架节点损伤识别问题,利用多标签分类的优势,构建了多标签单输出和多标签多输出两种卷积神经网络模型,用于框架结构节点损伤位置的判断和损伤程度诊断。针对复杂结构损伤位置判断时工况多、识别准确率不高等问题,提出了一种能对结构进行分层(或分区)处理并同时完成损伤诊断的多标签多输出卷积神经网络模型。分别构建了适用于多标签分类的浅层、深层和深层残差多输出卷积神经网络模型,并对其泛化性能进行了研究。结果表明:提出的模型具有较高的损伤诊断准确率和一定的抗噪能力,特别是经过分层(分区)处理后的多标签多输出网络模型更具高效性,有更快的收敛速度和更高的诊断准确率; 利用多标签多输出残差卷积神经网络模型可以从训练工况中提取到足够多的损伤信息,在面对未经过学习的工况时也能较准确判断各节点的损伤等级。

Abstract:

In order to study the damage diagnosis of complex spatial frame joints as the research object, two convolutional neural network models of multi-label single-output and multi-label multi-output were constructed by using the advantages of multi-label classification, which were used to judge the damage location and damage degree of frame structure joints. Aiming at the problems of multiple location conditions and low recognition accuracy of damage joints in complex structures, a multi-label multi-output convolutional neural network model was proposed, which could process the structure hierarchically(or partitioned)and complete the damage diagnosis at the same time. The shallow, deep and deep residual multi-output convolution neural network models for multi-label classification were constructed respectively, and their generalization performance was studied. The results show that the proposed model has high damage diagnosis accuracy and certain anti-noise performance. In particular, the multi-label multi-output network model after hierarchical(or partition)processing is more efficient, with faster convergence speed and higher diagnostic accuracy. Using the multi-label multi-output residual convolution neural network model, enough damage information can be extracted from the dynamic response, and the damage level of each joint can be accurately determined in the face of unlearned conditions.

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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(01):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(01):38.[doi:10.19815/j.jace.2022.02043]
[3]秦世强,苏 晟,杨 睿.基于多标签卷积神经网络的结构损伤识别[J].建筑科学与工程学报,2024,41(03):108.[doi:10.19815/j.jace.2022.07014]
　QIN Shiqiang,SU Sheng,YANG Rui.Structural damage identification based on multi-label convolution neural network[J].Journal of Architecture and Civil Engineering,2024,41(01):108.[doi:10.19815/j.jace.2022.07014]

备注/Memo

备注/Memo:

收稿日期:2023-02-05 投稿网址:http://jace.chd.edu.cn
基金项目:国家自然科学基金项目(52378313)
作者简介:李书进(1967-),男,工学博士,教授,博士生导师,E-mail:sjli@whut.edu.cn。
Author resume: LI Shujin(1967-), male, PhD, professor, E-mail: sjli@whut.edu.cn.

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更新日期/Last Update: 2025-01-20