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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[1] 李惠, 鲍跃全, 李顺龙, 等. 结构健康监测数据科学与工程[J]. 工程力学, 2015, 32(8): 1 − 7. doi: 10.6052/j.issn.1000-4750.2014.08.ST11LI Hui, BAO Yuequan, LI Shunlong, et al. Data science and engineering for structural health monitoring [J]. Engineering Mechanics, 2015, 32(8): 1 − 7. (in Chinese) doi: 10.6052/j.issn.1000-4750.2014.08.ST11 [2] 周颖, 张立迅, 刘彤, 等. 基于计算机视觉的结构系统识别[J]. 土木工程学报, 2018, 51(11): 17 − 23.ZHOU Ying, ZHANG Lixun, LIU Tong, et al. Structural system identification based on computer vision [J]. China Civil Engineering Journal, 2018, 51(11): 17 − 23. (in Chinese) [3] MA Z X, CHOI J, SOHN H. Real-time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements [J]. Computer-aided Civil and Infrastruc-ture Engineering, 2022, 37(6): 688 − 703. doi: 10.1111/mice.12767 [4] 郑翥鹏, 邱昊, 夏丹丹, 等. 未知激励下的无迹卡尔曼滤波新方法[J]. 工程力学, 2019, 36(6): 29 − 35. doi: 10.6052/j.issn.1000-4750.2018.05.0270ZHENG Zhupeng, QIU Hao, XIA Dandan, et al. A novel unscented Kalman filter with unknown input [J]. Engineering Mechanics, 2019, 36(6): 29 − 35. (in Chinese) doi: 10.6052/j.issn.1000-4750.2018.05.0270 [5] 单伽锃, 张寒青, 宫楠. 基于监测数据的结构地震损伤追踪与量化评估方法[J]. 工程力学, 2021, 38(1): 164 − 173. doi: 10.6052/j.issn.1000-4750.2020.03.0138SHAN Jiazeng, ZHANG Hanqing, GONG Nan. Tracking and quantitative evaluation of earthquake-induced structural damage using health monitoring data [J]. Engineering Mechanics, 2021, 38(1): 164 − 173. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.03.0138 [6] 吴斌, 王贞, 许国山, 等. 工程结构混合试验技术研究与应用进展[J]. 工程力学, 2022, 39(1): 1 − 20. doi: 10.6052/j.issn.1000-4750.2021.10.ST06WU Bin, WANG Zhen, XU Guoshan, et al. Research and application progress in hybrid testing of engineering structures [J]. Engineering Mechanics, 2022, 39(1): 1 − 20. (in Chinese) doi: 10.6052/j.issn.1000-4750.2021.10.ST06 [7] ABDELRAZAQ A. 哈利法塔结构性能和响应的验证: 足尺结构健康监测方案[J]. 建筑结构, 2014, 44(5): 87 − 97.ABDELRAZAQ A. Validating the structural behavior and response of Burj Khalifa: Full scale structural health monitoring programs [J]. Building Structure, 2014, 44(5): 87 − 97. (in Chinese) [8] 韩建平, 张一恒, 张鸿宇. 基于计算机视觉的振动台试验结构模型位移测量[J]. 地震工程与工程振动, 2019, 39(4): 22 − 29.HAN Jianping, ZHANG Yiheng, ZHANG Hongyu. Displacement measurement of shaking table test structure model based on computer vision [J]. Earthquake Engineering and Engineering Dynamics, 2019, 39(4): 22 − 29. (in Chinese) [9] 王亚荣, 黄声享, 匡翠林. 融合GNSS和加速度计监测数据的超高建筑动态特性分析[J]. 测绘通报, 2019(8): 14 − 19.WANG Yarong, HUANG Shengxiang, KUANG Cuilin. Fusion of GNSS and accelerometer monitoring data for dynamic monitoring of high-rise buildings [J]. Bulletin of Surveying and Mapping, 2019(8): 14 − 19. (in Chinese) [10] 鲍跃全, 李惠. 人工智能时代的土木工程[J]. 土木工程学报, 2019, 52(5): 1 − 11.BAO Yuequan, LI Hui. Artificial intelligence for civil engineering [J]. China Civil Engineering Journal, 2019, 52(5): 1 − 11. (in Chinese) [11] 杨娜, 张翀, 李天昊. 基于无人机与计算机视觉的中国古建筑木结构裂缝监测系统设计[J]. 工程力学, 2021, 38(3): 27 − 39. doi: 10.6052/j.issn.1000-4750.2020.04.0263YANG Na, ZHANG Chong, LI Tianhao. Design of crack monitoring system for Chinese ancient wooden buildings based on UAV and CV [J]. Engineering Mechanics, 2021, 38(3): 27 − 39. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.04.0263 [12] KIM H, SIM S H, BILLIE F S. Automated concrete crack evaluation using stereo vision with two different focal lengths [J]. Automation in Construction, 2022, 135: 104136. doi: 10.1016/j.autcon.2022.104136 [13] 朱前坤, 陈建邦, 张琼, 等. 基于计算机视觉人行桥挠度影响线非接触式识别[J]. 工程力学, 2021, 38(8): 145 − 153. doi: 10.6052/j.issn.1000-4750.2020.08.0557ZHU Qiankun, CHEN Jianbang, ZHANG Qiong, et al. A non-contact recognition for deflection influence line of footbridge based on computer vision [J]. Engineering Mechanics, 2021, 38(8): 145 − 153. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.08.0557 [14] 叶肖伟, 董传智. 基于计算机视觉的结构位移监测综述[J]. 中国公路学报, 2019, 32(11): 21 − 39.YE Xiaowei, DONG Chuanzhi. Review of computer vision-based structural displacement monitoring [J]. China Journal of Highway and Transport, 2019, 32(11): 21 − 39. (in Chinese) [15] 吕钧澔, 校金友, 文立华, 等. 基于视觉分区的结构振动模态测试方法[J]. 工程力学, 2022, 39(3): 249 − 256. doi: 10.6052/j.issn.1000-4750.2021.01.0058LYU Junhao, XIAO Jinyou, WEN Lihua, et al. A structural vibration modal testing method based on vision subareas [J]. Engineering Mechanics, 2022, 39(3): 249 − 256. (in Chinese) doi: 10.6052/j.issn.1000-4750.2021.01.0058 [16] YANG R Y, SHUBHENDU K S, TAVAKKOLI M, et al. CNN-LSTM deep learning architecture for computer vision-based modal frequency detection [J]. Mechanical Systems and Signal Processing, 2020, 144: 106885. doi: 10.1016/j.ymssp.2020.106885 [17] PARK J W, MOON D S, YOON H, et al. Visual-inertial displacement sensing using data fusion of vision-based displacement with acceleration [J]. Structural Control and Health Monitoring, 2018, 25(3): e2122. doi: 10.1002/stc.2122 [18] WANG J Q, ZHAO J, LIU Y W, et al. Vision‐based displacement and joint rotation tracking of frame structure using feature mix with single consumer‐grade camera [J]. Structural Control and Health Monitoring, 2021, 28(12): e2832. [19] KHUC T, C F N. Computer vision-based displacement and vibration monitoring without using physical target on structures [J]. Structure and Infrastructure Engineering, 2017, 13(4): 505 − 516. doi: 10.1080/15732479.2016.1164729 [20] LEE J J, SHINOZUKA M. A vision-based system for remote sensing of bridge displacement [J]. NDT & E International, 2006, 39(5): 425 − 431. [21] SMYTH A, WU M L. Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring [J]. Mechanical Systems and Signal Processing, 2007, 21(2): 706 − 723. [22] SIMON D. Optimal smoothing [M]// Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches. Hoboken, New Jersey, John Wiley & Sons, Inc, 2006: 263 − 296. [23] XU Y, BROWNJOHN J M W, HESTER D, et al. Long-span bridges: Enhanced data fusion of GPS displacement and deck accelerations [J]. Engineering Structures, 2017, 147: 639 − 651. doi: 10.1016/j.engstruct.2017.06.018 -
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