YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Performance of Constructed Facilities
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Performance of Constructed Facilities
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data

    Source: Journal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 006::page 04024046-1
    Author:
    Hanwen Ju
    ,
    Yang Deng
    ,
    Yingjie Zhao
    ,
    Ting-Hua Yi
    ,
    Guoqiang Zhong
    ,
    Aiqun Li
    DOI: 10.1061/JPCFEV.CFENG-4680
    Publisher: American Society of Civil Engineers
    Abstract: Automatic 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.
    • Download: (5.198Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4304148
    Collections
    • Journal of Performance of Constructed Facilities

    Show full item record

    contributor authorHanwen Ju
    contributor authorYang Deng
    contributor authorYingjie Zhao
    contributor authorTing-Hua Yi
    contributor authorGuoqiang Zhong
    contributor authorAiqun Li
    date accessioned2025-04-20T10:10:43Z
    date available2025-04-20T10:10:43Z
    date copyright9/13/2024 12:00:00 AM
    date issued2024
    identifier otherJPCFEV.CFENG-4680.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304148
    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.
    publisherAmerican Society of Civil Engineers
    titleAutomatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data
    typeJournal Article
    journal volume38
    journal issue6
    journal titleJournal of Performance of Constructed Facilities
    identifier doi10.1061/JPCFEV.CFENG-4680
    journal fristpage04024046-1
    journal lastpage04024046-14
    page14
    treeJournal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 006
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian