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    Automated Damage Localization and Quantification in Concrete Bridges Using Point Cloud-Based Surface-Fitting Strategy

    Source: Journal of Computing in Civil Engineering:;2021:;Volume ( 035 ):;issue: 006::page 04021028-1
    Author:
    Hyunjun Kim
    ,
    Jinyoung Yoon
    ,
    Jonghwa Hong
    ,
    Sung-Han Sim
    DOI: 10.1061/(ASCE)CP.1943-5487.0000993
    Publisher: ASCE
    Abstract: Digital image processing is considered an alternative to manual visual inspection, enabling automated damage evaluation for structural maintenance. Although advancements in artificial intelligence have improved identification performance, directly quantifying the surface damage in three-dimensional (3D) space using only two-dimensional (2D) images is difficult. In addition, because close-up images are preferred owing to the high measurement accuracy, its application requires a considerable amount of time to process numerous images of full-scale structure. In this study, a framework for automated damage evaluation using 3D laser scanning is presented. The proposed approach is designed to process the point clouds of a full-scale bridge by addressing different shapes. Furthermore, a tailored fitting strategy is employed to accurately identify the surface damage on the edge, which can cause false detections. In practice, the performance of the proposed framework is systematically validated on the point clouds of the bridge components.
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      Automated Damage Localization and Quantification in Concrete Bridges Using Point Cloud-Based Surface-Fitting Strategy

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4272053
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    contributor authorHyunjun Kim
    contributor authorJinyoung Yoon
    contributor authorJonghwa Hong
    contributor authorSung-Han Sim
    date accessioned2022-02-01T21:48:02Z
    date available2022-02-01T21:48:02Z
    date issued11/1/2021
    identifier other%28ASCE%29CP.1943-5487.0000993.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4272053
    description abstractDigital image processing is considered an alternative to manual visual inspection, enabling automated damage evaluation for structural maintenance. Although advancements in artificial intelligence have improved identification performance, directly quantifying the surface damage in three-dimensional (3D) space using only two-dimensional (2D) images is difficult. In addition, because close-up images are preferred owing to the high measurement accuracy, its application requires a considerable amount of time to process numerous images of full-scale structure. In this study, a framework for automated damage evaluation using 3D laser scanning is presented. The proposed approach is designed to process the point clouds of a full-scale bridge by addressing different shapes. Furthermore, a tailored fitting strategy is employed to accurately identify the surface damage on the edge, which can cause false detections. In practice, the performance of the proposed framework is systematically validated on the point clouds of the bridge components.
    publisherASCE
    titleAutomated Damage Localization and Quantification in Concrete Bridges Using Point Cloud-Based Surface-Fitting Strategy
    typeJournal Paper
    journal volume35
    journal issue6
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000993
    journal fristpage04021028-1
    journal lastpage04021028-14
    page14
    treeJournal of Computing in Civil Engineering:;2021:;Volume ( 035 ):;issue: 006
    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