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首页> 外文期刊>Structural Control and Health Monitoring >Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions
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Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions

机译:地震地面运动下电缆稳定模型塔振动的视频多尺度识别方法

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摘要

A camera can theoretically capture visual information of all target surfaces within its field of view. This capability allows a camera to act as a sensor for determining motion or deformation of targets at appropriate distances. Considering that there are so many cameras in public society (including cameras in smartphones) that can record the vibration of structures in an earthquake, this capability will be very helpful for assessing structural seismic damage post-earthquake. To this end, this paper proposes a two-step combination of the SCF (support correlation filters) algorithm with the KLT (Kanade-Lucas-Tomasi) algorithm for displacement identification of cable-stayed bridges from video with higher accuracy over SCF only and higher robustness over KLT only. The SCF algorithm can robustly and quickly track the target in the video, and the KLT algorithm can accurately track the pixel locations of targets. The shaking table test of a scale cable-stayed bridge model was conducted, and the vibration of the tower was recorded by a camera. The linear and nonlinear displacement responses of the tower under different earthquake ground motions were identified. The accuracy of the identified displacement response was validated through comparison with measurements from laser displacement transducers.
机译:摄像机理论上可以在其视野中理论上捕获所有目标表面的可视信息。这种能力允许相机用作用于在适当距离下确定目标的运动或变形的传感器。考虑到公共社会中有这么多相机(包括智能手机的相机),可以记录地震中结构的振动,这种能力对评估地震后结构性地震损伤非常有帮助。为此,本文提出了SCF(支持相关滤波器)算法与KLT(KANADE-LUCAS-TOMASI)算法的两步组合,用于从视频的缆车停留桥的位移识别,高精度,高于SCF和更高只对KLT的鲁棒性。 SCF算法可以鲁棒地和快速跟踪视频中的目标,并且KLT算法可以准确地跟踪目标的像素位置。进行了刻度斜拉桥模型的振动台测试,并通过相机记录塔的振动。鉴定了不同地震地面运动下塔的线性和非线性位移应答。通过与来自激光位移换能器的测量结果进行验证,验证了所识别的位移响应的准确性。

著录项

  • 来源
    《Structural Control and Health Monitoring》 |2019年第3期|e2314.1-e2314.19|共19页
  • 作者单位

    Harbin Inst Technol Sch Civil Engn Harbin 150090 Heilongjiang Peoples R China;

    Harbin Inst Technol Sch Civil Engn Harbin 150090 Heilongjiang Peoples R China|Harbin Inst Technol Key Lab Struct Dynam Behav & Control Minist Educ Harbin Heilongjiang Peoples R China|Harbin Inst Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Minist Ind & Informat Technol Harbin Heilongjiang Peoples R China;

    Tongji Univ State Key Lab Disaster Reduct Civil Engn Minist Educ Shanghai Peoples R China|Tongji Univ Sch Civil Engn Shanghai Peoples R China;

    Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China;

    Tongji Univ State Key Lab Disaster Reduct Civil Engn Minist Educ Shanghai Peoples R China|Tongji Univ Sch Civil Engn Shanghai Peoples R China;

    Harbin Inst Technol Sch Civil Engn Harbin 150090 Heilongjiang Peoples R China|Harbin Inst Technol Key Lab Struct Dynam Behav & Control Minist Educ Harbin Heilongjiang Peoples R China|Harbin Inst Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Minist Ind & Informat Technol Harbin Heilongjiang Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    bridges; computer vision; displacement measurement; seismic; support correlation filter;

    机译:桥梁;计算机视觉;位移测量;地震;支持相关滤波器;

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