一种基于RTK-GNSS技术的大跨径悬索桥动态特性分析方法
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摘要
为了研究一座大跨径自锚式悬索桥的动态特性,本文采用实时动态差分-全球卫星导航定位系统(RTKGNSS)和加速度计对环境激励下天津富民桥进行监测试验.考虑到RTK-GNSS系统定位精度的缺陷,提出了一种基于集合经验模态分解(EEMD)法与小波分解(WD)技术的联合降噪方法(EEMDWD),利用该方法对监测信号进行降噪处理以提高仪器的测量精度.随后,采用快速傅里叶变换(FFT)与随机减量技术(RDT)对降噪信号做进一步分析,从中获取结构的固有频率与相应的阻尼比.同时,为了与实测结果进行对比,建立了结构的有限元模型.分析结果表明:(1)采用EEMDWD联合降噪方法可以有效抑制背景噪声的影响,并且明显优于采用单独的EEMD或者WD分析的结果;(2)通过FFT分析,成功拾取到了结构的1阶固有频率,即f =0.5873 Hz,且RTK-GNSS与加速度计两类传感器的模态频率识别结果相一致,进一步验证了RTK-GNSS系统用于监测环境激励下大跨径桥梁动态响应的可行性;(3)通过RDT分析,成功拾取到了结构的阻尼比,即ξ=2.12%;(4)阻尼比实测值与有限元模型分析结果基本吻合,两者相差2.86%.
To investigate the dynamic characteristics of a self-anchored suspension bridge(i.e. the Tianjin Fumin Bridge),a real-time kinematic-global navigation satellite system(RTK-GNSS) and an accelerometer are employed to monitor the dynamic responses of the structure under ambient excitation. Considering the defect in positioning accuracy of RTK-GNSS,a combined method(EEMDWD) based on ensemble empirical mode decomposition(EEMD)and wavelet decomposition(WD) is put forward to improve the measurement accuracy of instruments. Subsequently,the natural frequency and the corresponding damping ratio of the structure are extracted based on the fast Fourier transform(FFT) and random decrement technique(RDT). Meanwhile,the finite element model(FEM) of the structure is established for comparison with the field measurement results. Finally,the results show the following.(1)The EEMDWD effectively restrains the background noise of RTK-GNSS sensors,and it outperforms the single EEMD and WD methods.(2)The first natural frequency of the structure is successfully derived via FFT analysis,i.e f=0.5873 Hz. Consistent results are obtained from the modal frequency identification using the RTK-GNSS and accelerometer,further verifying that the RTK-GNSS technique is feasible for monitoring the dynamic responses of long-span bridges under ambient excitation.(3)The damping ratio is obtained via RDT analysis,i.e ξ=2.12%.(4)The first natural frequency obtained experimentally via FFT analysis coincides with the predicted value based on FEM,with the difference of approximately 2.86% from each other.
To investigate the dynamic characteristics of a self-anchored suspension bridge(i.e. the Tianjin Fumin Bridge),a real-time kinematic-global navigation satellite system(RTK-GNSS) and an accelerometer are employed to monitor the dynamic responses of the structure under ambient excitation. Considering the defect in positioning accuracy of RTK-GNSS,a combined method(EEMDWD) based on ensemble empirical mode decomposition(EEMD)and wavelet decomposition(WD) is put forward to improve the measurement accuracy of instruments. Subsequently,the natural frequency and the corresponding damping ratio of the structure are extracted based on the fast Fourier transform(FFT) and random decrement technique(RDT). Meanwhile,the finite element model(FEM) of the structure is established for comparison with the field measurement results. Finally,the results show the following.(1)The EEMDWD effectively restrains the background noise of RTK-GNSS sensors,and it outperforms the single EEMD and WD methods.(2)The first natural frequency of the structure is successfully derived via FFT analysis,i.e f=0.5873 Hz. Consistent results are obtained from the modal frequency identification using the RTK-GNSS and accelerometer,further verifying that the RTK-GNSS technique is feasible for monitoring the dynamic responses of long-span bridges under ambient excitation.(3)The damping ratio is obtained via RDT analysis,i.e ξ=2.12%.(4)The first natural frequency obtained experimentally via FFT analysis coincides with the predicted value based on FEM,with the difference of approximately 2.86% from each other.
引文
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[22]Cole H A.Method and Apparatus for Measuring the Damping Characteristic of A Structure:USA,3620069[P].1971-11-16.
[2]Yu J Y,Zhu P,Xu B,et al.Experimental assessment of high sampling-rate robotic total station for monitoring bridge dynamic responses[J].Measurement,2017,104:60-69.
[3]Wang T,Tang Y S.Dynamic displacement monitoring of flexural structures with distributed long-gage macrostrain sensors[J].Advances in Mechanical Engineering,2017,9(4):1-12.
[4]Sekiya H,Kimura H,Miki C.Technique for determining bridge displacement response using MEMS accelerometers[J].Sensors,2016,16(2):257-266.
[5]Fenerci A,?iseth O,R?nnquist A.Long-term monitoring of wind field characteristics and dynamic response of a long-span suspension bridge in complex terrain[J].Engineering Structures,2017,147:269-284.
[6]Masri S F,Sheng L H,Caffrey J P,et al.Application of a web-enabled real-time structural health monitoring system for civil infrastructure systems[J].Smart Materials and Structures,2004,13(6):1269-1283.
[7]李宏男,伊廷华,伊晓东,等.基于RTK GPS技术的超高层结构风振观测[J].工程力学,2007,24(8):121-126.Li Hongnan,Yi Tinghua,Yi Xiaodong,et al.Measurement of wind induced response of tall building based on RTK GPS technology[J].Engineering Mechanics,2007,24(8):121-126(in Chinese).
[8]Xiong C B,Niu Y B.An investigation of the dynamic characteristics of super high-rise buildings using realtime kinematic-global navigation satellite system technology[J].Advances in Structural Engineering,2018,21(5):783-792.
[9]许昌,岳东杰.基于RTK-GPS技术的高索塔振动试验与分析[J].振动与冲击,2010,29(3):134-136.Xu Chang,Yue Dongjie.Ambient vibration test of high pylon based on RTK-GPS technology[J].Journal of Vibration and Shock,2010,29(3):134-136(in Chinese).
[10]Yi T H,Li H N,Gu M.Full-scale measurements of dynamic response of suspension bridge subjected to environmental loads using GPS technology[J].Science China Technological Sciences,2010,53(2):469-479.
[11]Elnabwy M T,Kaloop M R,Elbeltagi E.Talkha steel highway bridge monitoring and movement identification using RTK-GPS technique[J].Measurement,2013,46(10):4282-4292.
[12]Li X,Zhang X,Ren X,et al.Precise positioning with current multi-constellation global navigation satellite systems:GPS,GLONASS,Galileo and Bei Dou[J].Scientific Reports,2015,5:8328-8341.
[13]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Roval Society A:Mathematical Physical and Engineering Sciences,1998,454:903-995.
[14]Wu Zhaohua,Huang N E.Ensemble empirical mode decomposition:A noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1:1-41.
[15]张淑芳,黄小琴.基于背景消除的小波域视频质量评价模型[J].天津大学学报:自然科学与工程技术版,2017,50(12):1255-1261.Zhang Shufang,Huang Xiaoqin.Video quality assessment model in wavelet domain based on background subtraction[J].Journal of Tianjin University:Science and Technology,2017,50(12):1255-1261(in Chinese).
[16]焦敬品,于兆卿,刘文华,等.基于小波变换的薄层厚度电磁超声测量方法[J].仪器仪表学报,2013,34(3):588-595.Jiao Jingpin,Yu Zhaoqing,Liu Wenhua,et al.Electromagnetic acoustic thickness measurement method of thin liquid layer based on wavelet transform[J].Chinese Journal of Scientific Instrument,2013,34(3):588-595(in Chinese).
[17]Huang S C,Wu T K.Integrating recurrent SOM with wavelet-based kernel partial least square regressions for financial forecasting[J].Expert Systems with Applications,2010,37(8):5698-5705.
[18]吴一全,王志来.基于小波域改进SURF的遥感图像配准算法[J].天津大学学报:自然科学与工程技术版,2017,50(10):1084-1092.Wu Yiquan,Wang Zhilai.Remote sensing image registration algorithm based on improved SURF in wavelet domain[J].Journal of Tianjin University:Science and Technology,2017,50(10):1084-1092(in Chinese).
[19]Nakutis?,Ka?konas P.Bridge vibration logarithmic decrement estimation at the presence of amplitude beat[J].Measurement,2011,44(2):487-492.
[20]Huang N E,Wu Z H.A review on Hilbert-Huang transform:Method and its applications to geophysical studies[J].Reviews of Geophysics,2008,46(2):1-23.
[21]Brincker R,Zhang L,Andersen P.Modal identification from ambient responses using frequency domain decomposition[C]//Proceeding of 18th International Modal Analysis Conference.San Antonio,Texas,USA,2000:625-630.
[22]Cole H A.Method and Apparatus for Measuring the Damping Characteristic of A Structure:USA,3620069[P].1971-11-16.