Article(id=1322636263165907042, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1322636234858550134, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20200178, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1584460800000, receivedDateStr=2020-03-18, revisedDate=1586793600000, revisedDateStr=2020-04-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1793270658531, onlineDateStr=2026-10-29, pubDate=1603123200000, pubDateStr=2020-10-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793270658531, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793270658531, creator=13701087609, updateTime=1793270658531, updator=13701087609, issue=Issue{id=1322636234858550134, tenantId=1146029695717560320, journalId=1263187385517883426, year='2020', volume='48', issue='10', pageStart='1505', pageEnd='1688', issueExtLink='null', onlineDate='null', pubDate='1603123200000', pubDateStr='2020-10-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793270651783, creator='13701087609', updateTime=1793277146795, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322663476976267430, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1322636234858550134, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322663476976267431, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1322636234858550134, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1682, endPage=1688, ext={EN=ArticleExt(id=1322636263333679203, articleId=1322636263165907042, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Smart Detection of Connection Loosening of Frame Supported Glass Curtain Walls Based on Coupled Structural Response Vector and Support Vector Machine Approach, columnId=null, journalTitle=Journal of the Chinese Ceramic Society, columnName=null, runingTitle=null, highlight=null, articleAbstract=

A coupled structural response vector (SRV) and support vector machine (SVM) approach was developed for the connection loosening detection of frame supported glass curtain walls. Firstly, a structural simulation model was established, which considered both static and dynamic characteristics for the connection damage due to the loosening effect of frame supported glass walls. Secondly, the structural response vector was then used for damage pattern identification and quantification, which validated the effectiveness and applicability of SRV as damage indicators. Finally, a damage identification sample database containing different SRVs with the first four frequencies of glass panels and the inclination angles at nine points was established. Three SVMs with different kernels functions were used for the sample training and damaged identification. Particle swarm optimization was also utilized to optimize the parameters of radial basis function kernel. The results show that the effectiveness of coupled SRV-SVM approach for the identification and quantification of connection loosening of frame supported glass curtain walls is satisfactory.

, authors=Qiang ZHANG, Jian YANG, Xing'er WANG, Han XU, Yanran MENG, authorsList=Qiang ZHANG, Jian YANG, Xing'er WANG, Han XU, Yanran MENG, authorCompany=null, correspAuthors=Jian YANG, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322636263518228580, articleId=1322636263165907042, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于结构响应向量和支持向量机的框支承玻璃幕墙连接松弛状态智能识别, columnId=null, journalTitle=硅酸盐学报, columnName=, runingTitle=null, highlight=null, articleAbstract=

提出一种基于结构响应向量（SRV）和支持向量机（SVM）方法，对四边框支玻璃幕墙的连接松弛状态进行智能识别。首先，针对该类结构的动力和静力特性，建立四边框支玻璃幕墙有限元模型和在多种松弛状态下玻璃幕墙的SRV。其次，运用SRV对于边界松弛位置和程度进行识别，验证由动态分量和静态分量组成的SRV作为松弛指标的有效性和适用性。最后，以各松弛工况下的4阶频率和9个测点转角位移形成的SRV组成松弛识别样本库，运用3种不同核函数构成的SVM进行训练和识别，并运用粒子群算法对于SVM径向基（RBF）核函数的参数进行迭代优化，形成基于SRV-SVM的框支玻璃幕墙的连接松弛识别方法。结果表明：采用基于SRV-SVM的方法对四边框支玻璃幕墙连接松弛进行定位和定量描述可获得较理想的识别效果。

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