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基于视觉和振动监测数据融合的结构动态位移识别及其试验验证

修晟 张愿 单伽锃

修晟, 张愿, 单伽锃. 基于视觉和振动监测数据融合的结构动态位移识别及其试验验证[J]. 工程力学, 2023, 40(11): 90-98. doi: 10.6052/j.issn.1000-4750.2022.01.0106
引用本文: 修晟, 张愿, 单伽锃. 基于视觉和振动监测数据融合的结构动态位移识别及其试验验证[J]. 工程力学, 2023, 40(11): 90-98. doi: 10.6052/j.issn.1000-4750.2022.01.0106
XIU Cheng, ZHANG Yuan, SHAN Jia-zeng. VISION AND VIBRATION DATA FUSION-BASED STRUCTURAL DYNAMIC DISPLACEMENT MEASUREMENT WITH TEST VALIDATION[J]. Engineering Mechanics, 2023, 40(11): 90-98. doi: 10.6052/j.issn.1000-4750.2022.01.0106
Citation: XIU Cheng, ZHANG Yuan, SHAN Jia-zeng. VISION AND VIBRATION DATA FUSION-BASED STRUCTURAL DYNAMIC DISPLACEMENT MEASUREMENT WITH TEST VALIDATION[J]. Engineering Mechanics, 2023, 40(11): 90-98. doi: 10.6052/j.issn.1000-4750.2022.01.0106

基于视觉和振动监测数据融合的结构动态位移识别及其试验验证

doi: 10.6052/j.issn.1000-4750.2022.01.0106
基金项目: 国家自然科学基金项目(51878483,52278312);上海市青年科技启明星计划项目(20QC1400700)
详细信息
    作者简介:

    修 晟(1995−),男,黑龙江人,博士生,主要从事结构健康监测与防灾减灾研究(E-mail: xiucheng@tongji.edu.cn)

    张 愿(1999−),女,云南人,硕士生,主要从事结构健康监测与防灾减灾研究(E-mail: 2132210@tongji.edu.cn)

    通讯作者:

    单伽锃(1986−),男,浙江人,研究员,博士,博导,主要从事结构健康监测与防灾减灾研究(E-mail: jzshan@tongji.edu.cn)

  • 中图分类号: O329

VISION AND VIBRATION DATA FUSION-BASED STRUCTURAL DYNAMIC DISPLACEMENT MEASUREMENT WITH TEST VALIDATION

  • 摘要: 结构变形的动态测量与精准识别对于结构健康监测和性态评估具有重要意义。传统接触式位移监测需要在结构上布置传感器并设置相对独立稳定的位移参考系,考虑真实结构的复杂性,接触式测量和稳定参考系的做法限制了结构位移动力监测的工程应用。基于计算机视觉的结构位移监测方法具有非接触式测量、设备安装简单、成本相对低廉等优点,但现有的识别与追踪方法受限于光照条件、图像分辨率、拍摄帧率等因素,一定程度上限制了视觉方法的工程应用。针对加速度传感器在结构监测领域的广泛应用及其动态采样率高的优势,该文提出在数据层面融合“接触式加速度监测、非接触式位移识别”的概念,构建了结构关键位移响应的精准估计方法。通过一个1/2比例钢筋混凝土框架结构振动台试验,对提出的结构位移估计方法进行了动力试验验证。研究结果表明:对比单一视觉识别方法,该数据融合方法有效提高位移采样率,同时获得更丰富的结构宽频带振动响应与模态特征信息。
  • 图  1  数据融合流程

    Figure  1.  Flow chart of estimate scale factor

    图  2  大比例RC框架布局和实景

    Figure  2.  The large-scale RC frame and the region of interest (ROI)

    图  3  日本“3•11”地震工况下各层位移/像素的比例因子

    Figure  3.  The regressed scale factor between displacement and pixels for each floor under the Japan "3•11" excitation scenario

    图  4  两种方法下日本“3•11”地震工况下各楼层比例因子比较

    Figure  4.  Compare of scale factor under the Japan "3•11" excitation scenario

    图  5  日本“3•11”地震下各层峰值误差和归一化均方根误差

    Figure  5.  Peak error and NRMSE for each floor under the Japan "3•11" excitation scenario

    图  6  日本“3•11”地震下各层时程结果对比

    Figure  6.  Time history results for each floor under the Japan "3•11" excitation scenario

    图  7  Northridge地震工况下各层时程结果对比

    Figure  7.  Time history results for each floor under the Northridge excitation scenario

    图  8  上海人工波工况下各层时程结果对比

    Figure  8.  Time history results for each floor under the Shanghai artificial wave excitation scenario

    图  9  日本“3•11”地震工况下各楼层归一化功率谱密度

    Figure  9.  Normalized PSD for each floor under the Japan "3•11" excitation scenario

    图  10  Northridge地震工况下各楼层归一化功率谱密度

    Figure  10.  Normalized PSD for each floor under the Northridge excitation scenario

    图  11  上海人工波工况下各楼层归一化功率谱密度

    Figure  11.  Normalized PSD for each floor under the Shanghai artificial wave excitation scenario

    表  1  峰值误差统计

    Table  1.   Peak error statistics

    地震楼层卡尔曼滤波前/mm卡尔曼滤波后/mm
    日本“3•11”地震波13.862.25
    21.611.95
    31.510.72
    41.992.27
    Northridge地震波12.101.27
    21.360.08
    31.561.35
    42.622.36
    上海人工波12.751.63
    20.370.33
    30.601.12
    41.471.55
    下载: 导出CSV

    表  2  归一化均方根误差统计

    Table  2.   NRMSE statistics

    地震楼层卡尔曼滤波前卡尔曼滤波后相对误差
    日本“3•11”地震波10.420.390.07
    20.290.230.22
    30.180.120.32
    40.170.120.28
    Northridge地震波10.190.160.17
    20.200.170.14
    30.150.150.01
    40.220.22−0.03
    上海人工波10.200.150.25
    20.140.16−0.09
    30.120.070.38
    40.130.100.25
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-21
  • 修回日期:  2022-05-24
  • 录用日期:  2022-06-25
  • 网络出版日期:  2022-06-25
  • 刊出日期:  2023-11-25

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