基于UKF的结构动荷载识别方法与试验验证
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摘要
结构荷载识别是结构动力系统反问题研究的一个重要方面,而实际工程结构动力荷载的有效识别对结构设计、维护和加固具有重要的指导意义。利用基于UKF算法的两种荷载识别方法,即基于荷载时程正交分解的间接荷载识别法和直接荷载识别法,对一单自由度滞回非线性剪切模型进行数值仿真模拟,验证了直接荷载识别法应用于单自由度非线性模型荷载识别的合理性和有效性。对抗震实验室振动台进行空台加载,通过无线传感器测量振动台台面动力响应,利用UKF算法识别地震模拟振动台的刚度和阻尼参数。在已识别参数的基础上,采用直接荷载识别法对外部激励进行识别,并针对出现漂移现象的识别结果提出了位移-加速度联合观测值和噪声水平自适应评估两种直接扩展状态量的荷载识别方法,改善了识别过程的收敛性。
Load identification is an important aspect in inverse problem research of structural dynamic systems. The effective identification of structural dynamic load can provide an important guide for structure design, maintenance and reinforcement in practical engineering. Here, two load identification methods based on UKF algorithm including the indirect load identification method based on the orthogonal decomposition of load time history and the direct load identification one were used to do numerical simulation for a single-DOF hysteretic nonlinear shear model to verify the rationality and effectiveness of the direct load identification method applied in the load identification of the single-DOF nonlinear model. Firstly, an empty shaking table was loaded and its table surface's dynamic response was measured with a wireless sensor. Then, stiffness and damping parameters of the table were identified with UKF algorithm. After recognizing all parameters, the external load was identified using the direct load identification method. Aiming at the recognized results with "drift" phenomena, two methods to directly extend state variables including the joint observation of displacement and acceleration, and the noise level adaptive evaluation were proposed to improve the convergence of the recognition process.
Load identification is an important aspect in inverse problem research of structural dynamic systems. The effective identification of structural dynamic load can provide an important guide for structure design, maintenance and reinforcement in practical engineering. Here, two load identification methods based on UKF algorithm including the indirect load identification method based on the orthogonal decomposition of load time history and the direct load identification one were used to do numerical simulation for a single-DOF hysteretic nonlinear shear model to verify the rationality and effectiveness of the direct load identification method applied in the load identification of the single-DOF nonlinear model. Firstly, an empty shaking table was loaded and its table surface's dynamic response was measured with a wireless sensor. Then, stiffness and damping parameters of the table were identified with UKF algorithm. After recognizing all parameters, the external load was identified using the direct load identification method. Aiming at the recognized results with "drift" phenomena, two methods to directly extend state variables including the joint observation of displacement and acceleration, and the noise level adaptive evaluation were proposed to improve the convergence of the recognition process.
引文
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[15] 张延哲,车辆-轨道系统参数识别方法[D].哈尔滨:哈尔滨工业大学,2016.
[16] 赵博宇,丁勇,吴斌.基于扩展卡尔曼估计算法的地震模拟振动台模型识别[J].振动与冲击,2014,33(12):145-150.ZHAO Boyu,DING Yong,WU Bin.Identification of shaking table model for seismic simulation based on an extended Kalman estimator[J].Journal of Vibration and Shock,2014,33(12):145-150.
[2] YANG J N,PAN S,LIN S.Least square estimation with unknown excitations for damage identification of structures[J].Journal of Engineering Mechanics,2007,133(1):12-21.
[3] CORIGLIANO A,MARIANI S.Parameter identification in explicit structural dynamics:performance of the extended kalman filter[J].Computer Methods in Applied Mechanics and Engineering,2004,193(36/37/38):3807-3835.
[4] Dan Simon.最优状态估计-卡尔曼,H∞及非线性滤波[M].张勇刚,李宁,奔粤阳,译.北京:国防工业出版社,2013.
[5] CHATZI E N,SMYTH A W.The unscented kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing[J].Structural Control and Health Monitoring,2009,16:99-123.
[6] CHING J,BECK J L,PORTER K A,et al.Bayesian state estimation method for nonlinear systems and its application to recorded seismic response[J].Journal of Engineering Mechanics,2006,132(4):396-410.
[7] LI L,JI H,LUO J.The iterated extended kalman particle filter[J].Proceedings of the International Symposium on Communications and Information Technologies,2005:1172-1175.
[8] 张青霞,段忠东,Lukasz Jankowski.结构损伤和荷载的快速同步识别方法研究[J].振动与冲击,2013,32 (2):33-39.ZHANG Qingxia,DUAN Zhongdong,JANKOWSKI Lukasz .Fast simultaneous identification of structural damages and loads[J].Journal of Vibration and Shock,2013,32 (2):33-39.
[9] DING Y,ZHAO B Y,WU B,et al.Simultaneous Identification of structural parameter and external excitation with an improved unscented kalman filter[J].Advances in Structural Engineering,2015,18(11):1981-1998.
[10] DING Y,GUO L N,ZHAO B Y.Parameter identification for nonlinear structure by a constrained kalman filter with limited input information[J].International Journal of Structure Stability and Dynamics,2017,17(1):286-296.
[11] ASTROZA R,EBRAHIMIAN H,LI Y,et al.Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures[J].Mechanical Systems and Signal Processing,2017,93:661-687.
[12] STEFANOU G,PAPADRAKAKIS M.Assessment of spectral representation and karhunen-loève expansion methods for the simulation of gaussian stochastic fields[J].Computer Methods in Applied Mechanics and Engineering,2007,196(21):2465-2477.
[13] 李杰,刘章军.基于标准正交基的随机过程展开法[J].同济大学学报 (自然科学版),2006,34(10):1279-1283.LI Jie,LIU Zhangjun.Expansion method of stochastic processes based on normalized orthogonal bases[J].Journal of Tongji University (Natural Science) 2006,34(10):1279-1283.
[14] 李忠献,陈锋.基于梁格法的桥梁移动荷载识别[J].土木工程学报,2006,39(12):83-87.LI Zhongxian,CHEN Feng.Identification of moving loads on bridges using a grillage model[J].China Civil Engineering Journal,2006,39(12):83-87.
[15] 张延哲,车辆-轨道系统参数识别方法[D].哈尔滨:哈尔滨工业大学,2016.
[16] 赵博宇,丁勇,吴斌.基于扩展卡尔曼估计算法的地震模拟振动台模型识别[J].振动与冲击,2014,33(12):145-150.ZHAO Boyu,DING Yong,WU Bin.Identification of shaking table model for seismic simulation based on an extended Kalman estimator[J].Journal of Vibration and Shock,2014,33(12):145-150.