|Table of Contents|

Structural damage identification based on multi-label convolution neural network(PDF)

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

Issue:
2024年03期
Page:
108-119
Research Field:
建筑结构
Publishing date:

Info

Title:
Structural damage identification based on multi-label convolution neural network
Author(s):
QIN Shiqiang;  SU Sheng;  YANG Rui
(School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, Hubei, China)
Keywords:
structural damage identification;  convolution neural network;  multi-site damage;  multi-class classification;  multi-label classification
PACS:
TU317
DOI:
10.19815/j.jace.2022.07014
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.

References:

[1] 王凌波,王秋玲,朱 钊,等.桥梁健康监测技术研究现状及展望[J].中国公路学报,2021,34(12):25-45.
WANG Lingbo,WANG Qiuling,ZHU Zhao,et al.Current status and prospects of research on bridge health monitoring technology[J].China Journal of Highway and Transport,2021,34(12):25-45.
[2]《中国公路学报》编辑部.中国桥梁工程学术研究综述·2021[J].中国公路学报,2021,34(2):1-97.
Editorial Department of China Journal of Highway and Transport.Review on China's bridge engineering research:2021[J].China Journal of Highway and Transport,2021,34(2):1-97.
[3]翁 顺,朱宏平.基于有限元模型修正的土木结构损伤识别方法[J].工程力学,2021,38(3):1-16.
WENG Shun,ZHU Hongping.Damage identification of civil structures based on finite element model updating[J].Engineering Mechanics,2021,38(3):1-16.
[4]SALEHI H,BISWAS S,BURGUENO R.Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations[J].Engineering Applications of Artificial Intelligence,2019,86:136-153.
[5]BURGOS D A T,VARGAS R C G,PEDRAZA C,et al.Damage identification in structural health monitoring:a brief review from its implementation to the use of data-driven applications[J].Sensors,2020,20(3):733.
[6]孙利民,尚志强,夏 烨.大数据背景下的桥梁结构健康监测研究现状与展望[J].中国公路学报,2019,32(11):1-20.
SUN Limin,SHANG Zhiqiang,XIA Ye.Development and prospect of bridge structural health monitoring in the context of big data[J].China Journal of Highway and Transport,2019,32(11):1-20.
[7]SONY S,DUNPHY K,SADHU A,et al.A systematic review of convolutional neural network-based structural condition assessment techniques[J].Engineering Structures,2021,226:111347.
[8]O'SHEA K,NASH R.An introduction to convolutional neural networks[EB/OL].(2015-12-02)[2024-04-08].http://arxiv.org/abs/1511.08458
[9]李雪松,马宏伟,林逸洲.基于卷积神经网络的结构损伤识别[J].振动与冲击,2019,38(1):159-167.
LI Xuesong,MA Hongwei,LIN Yizhou.Structural damage identification based on convolution neural network[J].Journal of Vibration and Shock,2019,38(1):159-167.
[10]NGUYEN D H,NGUYEN Q B,BUI-TIEN T,et al.Damage detection in girder bridges using modal curvatures gapped smoothing method and convolutional neural network:application to Bo Nghi bridge[J].Theoretical and Applied Fracture Mechanics,2020,109:102728.
[11]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(6):29-37.
LI Shujin,ZHAO Yuan,KONG Fan,et al.Application of convolutional neural network in structural damage identification[J].Journal of Architecture and Civil Engineering,2020,37(6):29-37.
[12]ABDELJABER O,AVCI O,KIRANYAZ S,et al.Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks[J].Journal of Sound Vibration,2017,388:154-170.
[13]AVCI O,ABDELJABER O,KIRANYAZ S,et al.Structural damage detection in real time:implementation of 1D convolutional neural networks for SHM applications[C]//NIEZRECKI C.Proceedings of Structural Health Monitoring & Damage Detection.Cham:Springer,2017:49-54.
[14]LI M Y,JIA D W,WU Z Y,et al.Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network(CNN)[J].Mechanical Systems and Signal Processing,2022,165:108289.
[15]LI D,LIANG Z L,REN W X,et al.Structural damage identification under nonstationary excitations through recurrence plot and multi-label convolutional neural network[J].Measurement,2021,186:110101.
[16]ZHOU Z H,ZHANG M L,HUANG S J,et al.Multi-instance multi-label learning[J].Artificial Intelligence,2012,176(1):2291-2320.
[17]武红鑫,韩 萌,陈志强,等.监督和半监督学习下的多标签分类综述[J].计算机科学,2022,49(8):12-25.
WU Hongxin,HAN Meng,CHEN Zhiqiang,et al.Survey of multi-label classification based on supervised and semi-supervised learning[J].Computer Science,2022,49(8):12-25.
[18]ZHANG Z M,SUN C.Multi-site structural damage identification using a multi-label classification scheme of machine learning[J].Measurement,2020,154:107473.
[19]ZHANG M L,ZHOU Z H.Multi-label learning by instance differentiation[C]//ACM.Proceedings of the 22nd National Conference on Artificial Intelligence — Volume 1.Vancouver:ACM,2007:669-674.
[20]WEI X S,ZHOU Z H.An empirical study on image bag generators for multi-instance learning[J].Machine Language,2016,105(2):155-198.
[21]ALBAWI S,ABED MOHAMMED T,AL-ZAWI S.Understanding of a convolutional neural network[C]//IEEE.Proceedings of 2017 International Conference on Engineering and Technology(ICET).Antalya:IEEE,2017:1-6.
[22]ZHANG M L,ZHOU Z H.A review on multi-label learning algorithms[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(8):1819-1837.
[23]POWERS D M W.Evaluation:from precision,recall and F-measure to ROC,informedness,markedness and correlation[EB/OL].(2020-10-11)[2024-04-08].http://arxiv.org/abs/2010.16061.
[24]GHAMRAWI N,MCCALLUM A.Collective multi-label classification[C]//ACM.Proceedings of the 14th ACM International Conference on Information and Knowledge Management.Bremen:ACM,2005:195-200.
[25]CHRISTER A.Condition-based inspection models of major civil-engineering structures[J].Journal of the Operational Research Society,1988,39:71-82.
[26]单德山,罗凌峰,李 乔.桥梁健康监测2020年度研究进展[J].土木与环境工程学报(中英文),2021,43(增1):129-134.
SHAN Deshan,LUO Lingfeng,LI Qiao.Research progress of bridge health monitoring in 2020[J].Journal of Civil and Environmental Engineering,2021,43(S1):129-134.
[27]LAM H F,KO J M,WONG C W.Localization of damaged structural connections based on experimental modal and sensitivity analysis[J].Journal of Sound and Vibration,1998,210(1):91-115.
[28]JOHNSON E A,LAM H F,KATAFYGIOTIS L S,et al.Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data[J].Journal of Engineering Mechanics,2004,130(1):3-15.
[29]STEIN M.Large sample properties of simulations using Latin hypercube sampling[J].Technometrics,1987,29(2):143-151.
[30]QIN S Q,ZHANG Y Z,ZHOU Y L,et al.Dynamic model updating for bridge structures using the Kriging model and PSO algorithm ensemble with higher vibration modes[J].Sensors,2018,18(6):1879.

Memo

Memo:
-
Last Update: 2024-05-20