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    Structural Damage Detection Using Artificial Neural Networks

    Source: Journal of Infrastructure Systems:;1998:;Volume ( 004 ):;issue: 003
    Author:
    Jun Zhao
    ,
    John N. Ivan
    ,
    John T. DeWolf
    DOI: 10.1061/(ASCE)1076-0342(1998)4:3(93)
    Publisher: American Society of Civil Engineers
    Abstract: Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a counterpropagation neural network is used to locate structural damage for a beam, a frame, and support movements of a beam in its axial direction. The investigation considers a variety of diagnostic parameters, including static displacements, natural frequencies, mode shapes, and other parameters based on mode shapes. The method is first demonstrated on a plane frame, based on static displacements. It is then applied to continuous beams using dynamic properties of structures. The required data are obtained through computer simulation by finite-element analysis. The results demonstrate that these parameters can be used as diagnostic parameters for artificial neural networks in structural engineering. An anticipated application to bridge monitoring is discussed.
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      Structural Damage Detection Using Artificial Neural Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/48074
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    contributor authorJun Zhao
    contributor authorJohn N. Ivan
    contributor authorJohn T. DeWolf
    date accessioned2017-05-08T21:21:07Z
    date available2017-05-08T21:21:07Z
    date copyrightSeptember 1998
    date issued1998
    identifier other%28asce%291076-0342%281998%294%3A3%2893%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/48074
    description abstractArtificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a counterpropagation neural network is used to locate structural damage for a beam, a frame, and support movements of a beam in its axial direction. The investigation considers a variety of diagnostic parameters, including static displacements, natural frequencies, mode shapes, and other parameters based on mode shapes. The method is first demonstrated on a plane frame, based on static displacements. It is then applied to continuous beams using dynamic properties of structures. The required data are obtained through computer simulation by finite-element analysis. The results demonstrate that these parameters can be used as diagnostic parameters for artificial neural networks in structural engineering. An anticipated application to bridge monitoring is discussed.
    publisherAmerican Society of Civil Engineers
    titleStructural Damage Detection Using Artificial Neural Networks
    typeJournal Paper
    journal volume4
    journal issue3
    journal titleJournal of Infrastructure Systems
    identifier doi10.1061/(ASCE)1076-0342(1998)4:3(93)
    treeJournal of Infrastructure Systems:;1998:;Volume ( 004 ):;issue: 003
    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