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    Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization

    Source: Journal of Computing in Civil Engineering:;2019:;Volume ( 033 ):;issue: 003
    Author:
    Nur Sila Gulgec; Martin Takáč; Shamim N. Pakzad
    DOI: 10.1061/(ASCE)CP.1943-5487.0000820
    Publisher: American Society of Civil Engineers
    Abstract: Damage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the existence, location, and severity of damage. Therefore, this procedure requires complex data processing algorithms and dense sensor arrays, which brings computational intensity with it. To address this limitation, this paper introduces convolutional neural network (CNN), which is one of the major breakthroughs in image recognition, to the damage detection and localization problem. The CNN technique has the ability to discover abstract features and complex classifier boundaries that are able to distinguish various attributes of the problem. In this paper, a CNN topology was designed to classify simulated damaged and healthy cases and localize the damage when it exists. The performance of the proposed technique was evaluated through the finite-element simulations of undamaged and damaged structural connections. Samples were trained by using strain distributions as a consequence of various loads with several different crack scenarios. Completely new damage setups were introduced to the model during the testing process. Based on the findings of the proposed study, the damage diagnosis and localization were achieved with high accuracy, robustness, and computational efficiency.
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      Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4254736
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    contributor authorNur Sila Gulgec; Martin Takáč; Shamim N. Pakzad
    date accessioned2019-03-10T12:02:48Z
    date available2019-03-10T12:02:48Z
    date issued2019
    identifier other%28ASCE%29CP.1943-5487.0000820.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4254736
    description abstractDamage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the existence, location, and severity of damage. Therefore, this procedure requires complex data processing algorithms and dense sensor arrays, which brings computational intensity with it. To address this limitation, this paper introduces convolutional neural network (CNN), which is one of the major breakthroughs in image recognition, to the damage detection and localization problem. The CNN technique has the ability to discover abstract features and complex classifier boundaries that are able to distinguish various attributes of the problem. In this paper, a CNN topology was designed to classify simulated damaged and healthy cases and localize the damage when it exists. The performance of the proposed technique was evaluated through the finite-element simulations of undamaged and damaged structural connections. Samples were trained by using strain distributions as a consequence of various loads with several different crack scenarios. Completely new damage setups were introduced to the model during the testing process. Based on the findings of the proposed study, the damage diagnosis and localization were achieved with high accuracy, robustness, and computational efficiency.
    publisherAmerican Society of Civil Engineers
    titleConvolutional Neural Network Approach for Robust Structural Damage Detection and Localization
    typeJournal Paper
    journal volume33
    journal issue3
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000820
    page04019005
    treeJournal of Computing in Civil Engineering:;2019:;Volume ( 033 ):;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
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