{"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":"3 1","pages":""},"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}
引用次数: 0
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.
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.