VISION AND VIBRATION DATA FUSION-BASED STRUCTURAL DYNAMIC DISPLACEMENT MEASUREMENT WITH TEST VALIDATION
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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。Abstract: The dynamic measurement and identification of structural deformation are of great significance for structural health monitoring and performance assessment. Traditional contact-type displacement monitoring inevitably requires the arrangement of measurement points on physical structures as well as the setting of stable reference systems, limiting the application of dynamic displacement measurement of structures in practice. Computer vision-based structural displacement monitoring has the advantage of non-contact measurement, simple installation, and relatively low cost. However, the existing displacement identification methods are still influenced by lighting conditions, image resolution, and shooting-rate, which restricts its engineering applications. In order to take advantage of the high dynamic sampling rate of the traditional contact acceleration sensor, the concept of 'contact acceleration monitoring and non-contact displacement recognition' was proposed and an accurate estimation method for the critical dynamic deformation state of the structure was constructed. The proposed method is validated through a 1/2 scale reinforced concrete frame structure. The results show that the method can improve the displacement sampling rate and collect high-frequency vibration information compared with a single structural displacement visual measurement technique.
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Key words:
- structural monitoring /
- data fusion /
- computer vision /
- Kalman filter /
- scale factor
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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 1 3.86 2.25 2 1.61 1.95 3 1.51 0.72 4 1.99 2.27 Northridge地震波 1 2.10 1.27 2 1.36 0.08 3 1.56 1.35 4 2.62 2.36 上海人工波 1 2.75 1.63 2 0.37 0.33 3 0.60 1.12 4 1.47 1.55 
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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 1 0.42 0.39 0.07 2 0.29 0.23 0.22 3 0.18 0.12 0.32 4 0.17 0.12 0.28 Northridge地震波 1 0.19 0.16 0.17 2 0.20 0.17 0.14 3 0.15 0.15 0.01 4 0.22 0.22 −0.03 上海人工波 1 0.20 0.15 0.25 2 0.14 0.16 −0.09 3 0.12 0.07 0.38 4 0.13 0.10 0.25
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