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Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data
AbstractAutomatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring...
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Published in: | Journal of performance of constructed facilities 2024-12, Vol.38 (6) |
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container_title | Journal of performance of constructed facilities |
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creator | Ju, Hanwen Deng, Yang Zhao, Yingjie Yi, Ting-Hua Zhong, Guoqiang Li, Aiqun |
description | AbstractAutomatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable. |
doi_str_mv | 10.1061/JPCFEV.CFENG-4680 |
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However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable.</description><identifier>ISSN: 0887-3828</identifier><identifier>EISSN: 1943-5509</identifier><identifier>DOI: 10.1061/JPCFEV.CFENG-4680</identifier><language>eng</language><publisher>New York: American Society of Civil Engineers</publisher><subject>Accuracy ; Cable-stayed bridges ; Cables ; Clustering ; Damage detection ; Identification ; Identification methods ; Radio frequency ; Real time ; Structural health monitoring ; Technical Papers</subject><ispartof>Journal of performance of constructed facilities, 2024-12, Vol.38 (6)</ispartof><rights>2024 American Society of Civil Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a248t-f26f3be3dd23e1a643c6d038efe3c0cc8aebd0328ca4e891fff999e1e302ae0c3</cites><orcidid>0000-0001-5807-1440 ; 0000-0003-1680-4698</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4680$$EPDF$$P50$$Gasce$$H</linktopdf><linktohtml>$$Uhttp://ascelibrary.org/doi/abs/10.1061/JPCFEV.CFENG-4680$$EHTML$$P50$$Gasce$$H</linktohtml><link.rule.ids>314,780,784,3252,10068,27924,27925,76191,76199</link.rule.ids></links><search><creatorcontrib>Ju, Hanwen</creatorcontrib><creatorcontrib>Deng, Yang</creatorcontrib><creatorcontrib>Zhao, Yingjie</creatorcontrib><creatorcontrib>Yi, Ting-Hua</creatorcontrib><creatorcontrib>Zhong, Guoqiang</creatorcontrib><creatorcontrib>Li, Aiqun</creatorcontrib><title>Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data</title><title>Journal of performance of constructed facilities</title><description>AbstractAutomatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable.</description><subject>Accuracy</subject><subject>Cable-stayed bridges</subject><subject>Cables</subject><subject>Clustering</subject><subject>Damage detection</subject><subject>Identification</subject><subject>Identification methods</subject><subject>Radio frequency</subject><subject>Real time</subject><subject>Structural health monitoring</subject><subject>Technical Papers</subject><issn>0887-3828</issn><issn>1943-5509</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kDFPwzAQhS0EEqXwA9gsMafYcZo4YwltCWqBAVgtxzlXqVK72MnQf49DkJhY7nS6977TPYRuKZlRktL757ditfychfKyjpKUkzM0oXnCovmc5OdoQjjPIsZjfomuvN8TQuIszyYIFn1nD7JrFN7aWrZ45eCrB6NOuKzBdI1uVNhag63GD66pd4ALWbXgcW9qcLg0uh30MAgWlbHuEChba5rOusbs8KPs5DW60LL1cPPbp-hjtXwvnqLN67osFptIxgnvIh2nmlXA6jpmQGWaMJXWhHHQwBRRikuowhxzJRPgOdVa53kOFBiJJRDFpuhu5B6dDV_4Tuxt70w4KRgNIJLxOQsqOqqUs9470OLomoN0J0GJGNIUY5riJ00xpBk8s9EjvYI_6v-Gb_NbeOs</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Ju, Hanwen</creator><creator>Deng, Yang</creator><creator>Zhao, Yingjie</creator><creator>Yi, Ting-Hua</creator><creator>Zhong, Guoqiang</creator><creator>Li, Aiqun</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><orcidid>https://orcid.org/0000-0001-5807-1440</orcidid><orcidid>https://orcid.org/0000-0003-1680-4698</orcidid></search><sort><creationdate>20241201</creationdate><title>Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data</title><author>Ju, Hanwen ; Deng, Yang ; Zhao, Yingjie ; Yi, Ting-Hua ; Zhong, Guoqiang ; Li, Aiqun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a248t-f26f3be3dd23e1a643c6d038efe3c0cc8aebd0328ca4e891fff999e1e302ae0c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Cable-stayed bridges</topic><topic>Cables</topic><topic>Clustering</topic><topic>Damage detection</topic><topic>Identification</topic><topic>Identification methods</topic><topic>Radio frequency</topic><topic>Real time</topic><topic>Structural health monitoring</topic><topic>Technical Papers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ju, Hanwen</creatorcontrib><creatorcontrib>Deng, Yang</creatorcontrib><creatorcontrib>Zhao, Yingjie</creatorcontrib><creatorcontrib>Yi, Ting-Hua</creatorcontrib><creatorcontrib>Zhong, Guoqiang</creatorcontrib><creatorcontrib>Li, Aiqun</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of performance of constructed facilities</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ju, Hanwen</au><au>Deng, Yang</au><au>Zhao, Yingjie</au><au>Yi, Ting-Hua</au><au>Zhong, Guoqiang</au><au>Li, Aiqun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data</atitle><jtitle>Journal of performance of constructed facilities</jtitle><date>2024-12-01</date><risdate>2024</risdate><volume>38</volume><issue>6</issue><issn>0887-3828</issn><eissn>1943-5509</eissn><abstract>AbstractAutomatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable.</abstract><cop>New York</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/JPCFEV.CFENG-4680</doi><orcidid>https://orcid.org/0000-0001-5807-1440</orcidid><orcidid>https://orcid.org/0000-0003-1680-4698</orcidid></addata></record> |
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subjects | Accuracy Cable-stayed bridges Cables Clustering Damage detection Identification Identification methods Radio frequency Real time Structural health monitoring Technical Papers |
title | Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data |
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