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    Nonparametric Structural Damage Detection Algorithm for Ambient Vibration Response: Utilizing Artificial Neural Networks and Self-Organizing Maps

    Source: Journal of Architectural Engineering:;2016:;Volume ( 022 ):;issue: 002
    Author:
    Osama Abdeljaber
    ,
    Onur Avci
    DOI: 10.1061/(ASCE)AE.1943-5568.0000205
    Publisher: American Society of Civil Engineers
    Abstract: This study presentes a new nonparametric structural damage detection algorithm that integrates self-organizing maps with a pattern-recognition neural network to quantify and locate structural damage. In this algorithm, self-organizing maps are used to extract a number of damage indices from the ambient vibration response of the monitored structure. The presented study is unique because it demonstrates the development of a nonparametric vibration-based damage detection algorithm that utilizes self-organizing maps to extract meaningful damage indices from ambient vibration signals in the time domain. The ability of the algorithm to identify damage was demonstrated analytically using a finite-element model of a hot-rolled steel grid structure. The algorithm successfully located the structural damage under several damage cases, including damage resulting from local stiffness loss in members and damage resulting from changes in boundary conditions. A sensitivity study was also conducted to evaluate the effects of noise on the computed damage indices. The algorithm was proved to be successful even when the signals are noise-contaminated.
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      Nonparametric Structural Damage Detection Algorithm for Ambient Vibration Response: Utilizing Artificial Neural Networks and Self-Organizing Maps

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4242195
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    • Journal of Architectural Engineering

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    contributor authorOsama Abdeljaber
    contributor authorOnur Avci
    date accessioned2017-12-16T09:23:07Z
    date available2017-12-16T09:23:07Z
    date issued2016
    identifier other%28ASCE%29AE.1943-5568.0000205.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4242195
    description abstractThis study presentes a new nonparametric structural damage detection algorithm that integrates self-organizing maps with a pattern-recognition neural network to quantify and locate structural damage. In this algorithm, self-organizing maps are used to extract a number of damage indices from the ambient vibration response of the monitored structure. The presented study is unique because it demonstrates the development of a nonparametric vibration-based damage detection algorithm that utilizes self-organizing maps to extract meaningful damage indices from ambient vibration signals in the time domain. The ability of the algorithm to identify damage was demonstrated analytically using a finite-element model of a hot-rolled steel grid structure. The algorithm successfully located the structural damage under several damage cases, including damage resulting from local stiffness loss in members and damage resulting from changes in boundary conditions. A sensitivity study was also conducted to evaluate the effects of noise on the computed damage indices. The algorithm was proved to be successful even when the signals are noise-contaminated.
    publisherAmerican Society of Civil Engineers
    titleNonparametric Structural Damage Detection Algorithm for Ambient Vibration Response: Utilizing Artificial Neural Networks and Self-Organizing Maps
    typeJournal Paper
    journal volume22
    journal issue2
    journal titleJournal of Architectural Engineering
    identifier doi10.1061/(ASCE)AE.1943-5568.0000205
    treeJournal of Architectural Engineering:;2016:;Volume ( 022 ):;issue: 002
    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
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