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    A Detection Method for Bridge Cables Based on Intelligent Image Recognition and Magnetic-Memory Technology

    Source: Journal of Performance of Constructed Facilities:;2022:;Volume ( 036 ):;issue: 006::page 04022059
    Author:
    Qingling Meng
    ,
    Yun Zhang
    ,
    Hailiang Wang
    ,
    Xin Huang
    ,
    Zhenyu Wang
    DOI: 10.1061/(ASCE)CF.1943-5509.0001773
    Publisher: ASCE
    Abstract: In recent years, more and more disasters caused by the fracture of bridge cables have been reported. Brittle fracture of cables is mainly caused by corrosion fatigue, and thus it is crucial to find cable defects early. The present study developed a cable inspection method embedded with a lightweight deep-learning model and equipped with micromagnetic sensors, based on intelligent image recognition and magnetic memory technology. After four cable-stayed bridges were found with defects, five types of defects and features on the surface of cables were identified by the SqueezeNet network model with the image denoising algorithm and transfer-learning method, with accuracy of 97.18%. The corrosion along the cable was positioned with micromagnetic sensors. Four alerting levels were proposed and corresponding remedial measures were suggested to be implemented. The novelty of this work lies in the intelligent detection of bridge defects, as well as accurate evaluation of long-term performance of bridge cables.
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      A Detection Method for Bridge Cables Based on Intelligent Image Recognition and Magnetic-Memory Technology

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4289532
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    contributor authorQingling Meng
    contributor authorYun Zhang
    contributor authorHailiang Wang
    contributor authorXin Huang
    contributor authorZhenyu Wang
    date accessioned2023-04-07T00:40:46Z
    date available2023-04-07T00:40:46Z
    date issued2022/12/01
    identifier other%28ASCE%29CF.1943-5509.0001773.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4289532
    description abstractIn recent years, more and more disasters caused by the fracture of bridge cables have been reported. Brittle fracture of cables is mainly caused by corrosion fatigue, and thus it is crucial to find cable defects early. The present study developed a cable inspection method embedded with a lightweight deep-learning model and equipped with micromagnetic sensors, based on intelligent image recognition and magnetic memory technology. After four cable-stayed bridges were found with defects, five types of defects and features on the surface of cables were identified by the SqueezeNet network model with the image denoising algorithm and transfer-learning method, with accuracy of 97.18%. The corrosion along the cable was positioned with micromagnetic sensors. Four alerting levels were proposed and corresponding remedial measures were suggested to be implemented. The novelty of this work lies in the intelligent detection of bridge defects, as well as accurate evaluation of long-term performance of bridge cables.
    publisherASCE
    titleA Detection Method for Bridge Cables Based on Intelligent Image Recognition and Magnetic-Memory Technology
    typeJournal Article
    journal volume36
    journal issue6
    journal titleJournal of Performance of Constructed Facilities
    identifier doi10.1061/(ASCE)CF.1943-5509.0001773
    journal fristpage04022059
    journal lastpage04022059_11
    page11
    treeJournal of Performance of Constructed Facilities:;2022:;Volume ( 036 ):;issue: 006
    contenttypeFulltext
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