YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Bridge Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Bridge Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Multirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions

    Source: Journal of Bridge Engineering:;2024:;Volume ( 029 ):;issue: 011::page 04024081-1
    Author:
    Jian Guo
    ,
    Zejun Liang
    ,
    Kaijiang Ma
    ,
    Jiyi Wu
    DOI: 10.1061/JBENF2.BEENG-6880
    Publisher: American Society of Civil Engineers
    Abstract: When a ship–bridge collision occurs, prompt assessment of substructure damage is crucial. This study presents a novel approach for ship–bridge collision damage identification, addressing challenges inherent in traditional monitoring systems. The method overcomes issues such as complex installation, low efficiency, and high costs through a unique combination of the unscented Kalman filter (UKF) and computer vision technique. The approach exerts the structural equation of motion to derive a multirate UKF in the impact process, thereby identifying the stiffness of structures. Displacement and acceleration are fused to enhance the sampling rate of vision-measured displacement. Firstly, it monitors low sampling rate displacements on piers using computer vision, complemented by high-rate accelerometer data at the collision point. Secondly, displacement and acceleration data are integrated using a multirate UKF, addressing the challenge of image storage pressure associated with vision-based measurements. Finally, validation using finite-element and experimental models confirms the effectiveness of the approach in identifying substructure stiffness and recovering lost vibration characteristics. In experiment validation, the influence of computer vision algorithms and camera shooting distance on displacement monitoring and stiffness identification is also discussed separately. This approach provides a cost-effective and efficient solution for ship–bridge collision damage identification, contributing to advancements in the field of ship–bridge collision monitoring.
    • Download: (3.072Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Multirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4304988
    Collections
    • Journal of Bridge Engineering

    Show full item record

    contributor authorJian Guo
    contributor authorZejun Liang
    contributor authorKaijiang Ma
    contributor authorJiyi Wu
    date accessioned2025-04-20T10:34:36Z
    date available2025-04-20T10:34:36Z
    date copyright8/19/2024 12:00:00 AM
    date issued2024
    identifier otherJBENF2.BEENG-6880.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304988
    description abstractWhen a ship–bridge collision occurs, prompt assessment of substructure damage is crucial. This study presents a novel approach for ship–bridge collision damage identification, addressing challenges inherent in traditional monitoring systems. The method overcomes issues such as complex installation, low efficiency, and high costs through a unique combination of the unscented Kalman filter (UKF) and computer vision technique. The approach exerts the structural equation of motion to derive a multirate UKF in the impact process, thereby identifying the stiffness of structures. Displacement and acceleration are fused to enhance the sampling rate of vision-measured displacement. Firstly, it monitors low sampling rate displacements on piers using computer vision, complemented by high-rate accelerometer data at the collision point. Secondly, displacement and acceleration data are integrated using a multirate UKF, addressing the challenge of image storage pressure associated with vision-based measurements. Finally, validation using finite-element and experimental models confirms the effectiveness of the approach in identifying substructure stiffness and recovering lost vibration characteristics. In experiment validation, the influence of computer vision algorithms and camera shooting distance on displacement monitoring and stiffness identification is also discussed separately. This approach provides a cost-effective and efficient solution for ship–bridge collision damage identification, contributing to advancements in the field of ship–bridge collision monitoring.
    publisherAmerican Society of Civil Engineers
    titleMultirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions
    typeJournal Article
    journal volume29
    journal issue11
    journal titleJournal of Bridge Engineering
    identifier doi10.1061/JBENF2.BEENG-6880
    journal fristpage04024081-1
    journal lastpage04024081-17
    page17
    treeJournal of Bridge Engineering:;2024:;Volume ( 029 ):;issue: 011
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian