从改进的主成分分析和结构状态识别中提取模态参数

IF 2.1 4区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY Advances in Structural Engineering Pub Date : 2024-08-21 DOI:10.1177/13694332241269246
Xueyan Li, E. Liu, Lixin Wang, SJ Lin, W Zhao
{"title":"从改进的主成分分析和结构状态识别中提取模态参数","authors":"Xueyan Li, E. Liu, Lixin Wang, SJ Lin, W Zhao","doi":"10.1177/13694332241269246","DOIUrl":null,"url":null,"abstract":"With the vigorous development of building structures and important infrastructure, structural health monitoring is necessary. Because there is no need to establish structural finite element modeling and train for various structural conditions, the data-driven and unsupervised learning method is very popular. Principal component analysis is a powerful signal analysis tool, but its lack of physical significance and the loss of sensitive information have hindered its wider application. Therefore, the improved principal component analysis based narrowband filtering is proposed to extract mode shapes and construct the structural state vectors, so that the damage index is more sensitive to damage and robust to the environmental factors. After the vibration response of the long-term monitoring is analyzed by the principal component analysis, the Gaussian mixture model clustering analysis is used to classify the structural states. Finally, the proposed method is applied to the analysis of the simulation data of ASCE Benchmark structure and the measured data of steel beams in the lab. The results show that the structural state vector is sensitive to structural damage. The clustering analysis of Gaussian mixture model can distinguish the structural states. The effectiveness of the proposed method is verified.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modal parameter extraction from improved principal component analysis and structural state identification\",\"authors\":\"Xueyan Li, E. Liu, Lixin Wang, SJ Lin, W Zhao\",\"doi\":\"10.1177/13694332241269246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the vigorous development of building structures and important infrastructure, structural health monitoring is necessary. Because there is no need to establish structural finite element modeling and train for various structural conditions, the data-driven and unsupervised learning method is very popular. Principal component analysis is a powerful signal analysis tool, but its lack of physical significance and the loss of sensitive information have hindered its wider application. Therefore, the improved principal component analysis based narrowband filtering is proposed to extract mode shapes and construct the structural state vectors, so that the damage index is more sensitive to damage and robust to the environmental factors. After the vibration response of the long-term monitoring is analyzed by the principal component analysis, the Gaussian mixture model clustering analysis is used to classify the structural states. Finally, the proposed method is applied to the analysis of the simulation data of ASCE Benchmark structure and the measured data of steel beams in the lab. The results show that the structural state vector is sensitive to structural damage. The clustering analysis of Gaussian mixture model can distinguish the structural states. The effectiveness of the proposed method is verified.\",\"PeriodicalId\":50849,\"journal\":{\"name\":\"Advances in Structural Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Structural Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/13694332241269246\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Structural Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/13694332241269246","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

随着建筑结构和重要基础设施的蓬勃发展,结构健康监测十分必要。由于无需建立结构有限元模型和针对各种结构条件进行训练,数据驱动和无监督学习方法非常受欢迎。主成分分析是一种功能强大的信号分析工具,但其缺乏物理意义和丢失敏感信息的缺点阻碍了它的广泛应用。因此,提出了基于窄带滤波的改进型主成分分析法,提取模态振型,构建结构状态向量,使损伤指数对损伤更加敏感,对环境因素更加稳健。通过主成分分析对长期监测的振动响应进行分析后,利用高斯混合模型聚类分析对结构状态进行分类。最后,将所提出的方法应用于分析 ASCE 基准结构的模拟数据和实验室钢梁的测量数据。结果表明,结构状态向量对结构损伤很敏感。高斯混合模型的聚类分析可以区分结构状态。验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Modal parameter extraction from improved principal component analysis and structural state identification
With the vigorous development of building structures and important infrastructure, structural health monitoring is necessary. Because there is no need to establish structural finite element modeling and train for various structural conditions, the data-driven and unsupervised learning method is very popular. Principal component analysis is a powerful signal analysis tool, but its lack of physical significance and the loss of sensitive information have hindered its wider application. Therefore, the improved principal component analysis based narrowband filtering is proposed to extract mode shapes and construct the structural state vectors, so that the damage index is more sensitive to damage and robust to the environmental factors. After the vibration response of the long-term monitoring is analyzed by the principal component analysis, the Gaussian mixture model clustering analysis is used to classify the structural states. Finally, the proposed method is applied to the analysis of the simulation data of ASCE Benchmark structure and the measured data of steel beams in the lab. The results show that the structural state vector is sensitive to structural damage. The clustering analysis of Gaussian mixture model can distinguish the structural states. The effectiveness of the proposed method is verified.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in Structural Engineering
Advances in Structural Engineering 工程技术-工程:土木
CiteScore
5.00
自引率
11.50%
发文量
230
审稿时长
2.3 months
期刊介绍: Advances in Structural Engineering was established in 1997 and has become one of the major peer-reviewed journals in the field of structural engineering. To better fulfil the mission of the journal, we have recently decided to launch two new features for the journal: (a) invited review papers providing an in-depth exposition of a topic of significant current interest; (b) short papers reporting truly new technologies in structural engineering.
期刊最新文献
Multi-experimental seismic analysis of low-rise thin reinforced concrete wall building with unconnected elastomeric isolators using real-time hybrid simulations Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II Deep learning-based minute-scale digital prediction model for temperature induced deflection of a multi-tower double-layer steel truss bridge Experimental investigation on shear behavior of double-row perforated GFRP rib connectors in FRP-concrete hybrid beams Seismic response prediction method of train-bridge coupled system based on convolutional neural network-bidirectional long short-term memory-attention modeling
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1