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    An Automatic Modal Identification Framework for Civil Structures Based on Deep Learning and Frequency-Damping Heatmaps

    Source: Journal of Structural Engineering:;2025:;Volume ( 151 ):;issue: 005::page 04025040-1
    Author:
    Kang Xu
    ,
    Qiu-Sheng Li
    DOI: 10.1061/JSENDH.STENG-14181
    Publisher: American Society of Civil Engineers
    Abstract: Real-time estimation of modal parameters from dynamic responses is important for structural health monitoring of civil structures, facilitating the rapid development of automatic modal identification algorithms. However, existing algorithms still involve human interaction and utilize image information to a limited extent. To fill this gap, this paper proposes a computer vision-based automatic modal identification framework combining stochastic subspace identification (SSI) and faster region-based convolutional network (Faster R-CNN), which can directly estimate modal parameters from images. Specifically, the SSI method is first used to generate modal parameter candidates (including physical and spurious modes), based on which the frequency-damping images containing rich visual information on modal parameters can be obtained. Then, the Faster R-CNN is employed to obtain physical modes from the images. Finally, the modal parameters of each structural mode are obtained by sorting the extracted physical modes by natural frequencies. The proposed framework is trained and validated through numerical simulation studies. Besides, the trained framework is applied to automatically identify modal parameters of a 600-m-tall supertall building during a typhoon event. This paper aims to develop an automatic algorithm for estimating modal parameters of civil structures and to promote the application of computer vision in the field of automatic modal identification.
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      An Automatic Modal Identification Framework for Civil Structures Based on Deep Learning and Frequency-Damping Heatmaps

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306778
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    • Journal of Structural Engineering

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    contributor authorKang Xu
    contributor authorQiu-Sheng Li
    date accessioned2025-08-17T22:19:48Z
    date available2025-08-17T22:19:48Z
    date copyright5/1/2025 12:00:00 AM
    date issued2025
    identifier otherJSENDH.STENG-14181.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306778
    description abstractReal-time estimation of modal parameters from dynamic responses is important for structural health monitoring of civil structures, facilitating the rapid development of automatic modal identification algorithms. However, existing algorithms still involve human interaction and utilize image information to a limited extent. To fill this gap, this paper proposes a computer vision-based automatic modal identification framework combining stochastic subspace identification (SSI) and faster region-based convolutional network (Faster R-CNN), which can directly estimate modal parameters from images. Specifically, the SSI method is first used to generate modal parameter candidates (including physical and spurious modes), based on which the frequency-damping images containing rich visual information on modal parameters can be obtained. Then, the Faster R-CNN is employed to obtain physical modes from the images. Finally, the modal parameters of each structural mode are obtained by sorting the extracted physical modes by natural frequencies. The proposed framework is trained and validated through numerical simulation studies. Besides, the trained framework is applied to automatically identify modal parameters of a 600-m-tall supertall building during a typhoon event. This paper aims to develop an automatic algorithm for estimating modal parameters of civil structures and to promote the application of computer vision in the field of automatic modal identification.
    publisherAmerican Society of Civil Engineers
    titleAn Automatic Modal Identification Framework for Civil Structures Based on Deep Learning and Frequency-Damping Heatmaps
    typeJournal Article
    journal volume151
    journal issue5
    journal titleJournal of Structural Engineering
    identifier doi10.1061/JSENDH.STENG-14181
    journal fristpage04025040-1
    journal lastpage04025040-12
    page12
    treeJournal of Structural Engineering:;2025:;Volume ( 151 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian