桥梁结构物理参数识别的双单元子结构法
|
摘要
为了解决大型桥梁结构局部区域的健康监测问题,提出了一种用于桥梁结构物理参数识别的双单元子结构法。该方法是一种基于有限元的时域系统识别方法,其一次仅能识别出两个相邻单元的物理参数。将桥梁结构划分为若干个单元,选取任意两相邻单元为研究对象,建立其状态方程和观测方程。应用广义卡尔曼滤波,可得到该两相邻单元的质量、刚度和阻尼等物理参数。以一座三跨连续梁桥为例进行仿真分析,识别出不同信噪比情况下其子结构的物理参数。结果表明参数识别精度高,收敛速度快,从而验证了方法的有效性。
In order to solve the problem of health monitoring at local area of large bridges, a double-element substructure method to identify the physical parameters of bridge structures is proposed. This method is a finite element-based time domain system identification approach, in which the physical parameters of only two adjacent elements can be identified each time. In the paper, a bridge structure is divided into finite elements. The state and observation equations are established for arbitrary two adjacent elements. Using the extended Kalman filter, the physical parameters including stiffness, mass and damping of the substructure are obtained. Taking a 3-span continuous beam bridge as an example for simulation, the physical parameters of the substructure of the bridge are identified under different noise levels. The results show that the parameters can be identified correctly and efficiently, which proves the effectiveness of the proposed method.
In order to solve the problem of health monitoring at local area of large bridges, a double-element substructure method to identify the physical parameters of bridge structures is proposed. This method is a finite element-based time domain system identification approach, in which the physical parameters of only two adjacent elements can be identified each time. In the paper, a bridge structure is divided into finite elements. The state and observation equations are established for arbitrary two adjacent elements. Using the extended Kalman filter, the physical parameters including stiffness, mass and damping of the substructure are obtained. Taking a 3-span continuous beam bridge as an example for simulation, the physical parameters of the substructure of the bridge are identified under different noise levels. The results show that the parameters can be identified correctly and efficiently, which proves the effectiveness of the proposed method.
引文
[1]Zong Z H,Wang TL,Huang D Z,Zheng Z F.State-of-the-art report of bridge health monitoring[J].Journal of Fuzhou University,2002,30(2):127~152.
[2]Hoshiya M,Statio E.Structural identification by extended Kalman filter[J].Journal of Engineering Mechanics,ASCE,1984,110(12):1757~1770.
[3]Loh C H,Tsaur Y H.Time domain estimation of structural parameters[J].Engineering Structures,1988,10(2):95~105.
[4]尚久铨.卡尔曼滤波法在结构动态参数估计中的应用[J].地震工程与工程震动,1991,11(2):62~72.Shang Jiuquan.Application of Kalman filtering method to estimate structural dynamic parameters[J].Earthquake Engineering and Engineering Vibration,1991,11(2):62~72.(in Chinese)
[5]Hoshiya M,Sutoh A.Kalman filter-finite element method in identification[J].Journal of Engineering Mechanics,ASCE,1993,119(2):197~210.
[6]Ghanem R,Shinozuka M.Structural system identification I:theory[J].Journal of Engineering Mechanics,ASCE,1995,121(2):255~264.
[7]Ghanem R,Shinozuka M.Structural system identification II:experimental verification[J].Journal of Engineering Mechanics,ASCE,1995,121(2):265~273.
[8]孙清,张陵,张爱社,伍晓红.基于扩展卡尔曼滤波(EKF)的结构动态物理参数识别[J].工程力学,2001,(增刊):224~230.Sun Qing,Zhang Ling,Zhang Aishe,Wu Xiaohong.Identification for structural dynamic physical parameters using Extended Kalman Filter(EKF)[J].Engineering Mechanics,2001,(Sup.):224~230.(in Chinese)
[9]Koh C G,See L M,Balendra T.Estimation of structural parameters in time domain:a substructure approach[J].Earthquake Eng.and Struc.Dynamics,1991,20:787~801.
[10]Oreta A C,Tanabe T.Localized identification of structures by Kalman filter[J].Structural Eng./Earthquake Eng.,1993,9(4):217~225.
[11]Oreta A C,Tanabe T.Element identification of member properties of framed structures[J].Journal of Structural Engineering,1994,120(7):1961~1976.
[2]Hoshiya M,Statio E.Structural identification by extended Kalman filter[J].Journal of Engineering Mechanics,ASCE,1984,110(12):1757~1770.
[3]Loh C H,Tsaur Y H.Time domain estimation of structural parameters[J].Engineering Structures,1988,10(2):95~105.
[4]尚久铨.卡尔曼滤波法在结构动态参数估计中的应用[J].地震工程与工程震动,1991,11(2):62~72.Shang Jiuquan.Application of Kalman filtering method to estimate structural dynamic parameters[J].Earthquake Engineering and Engineering Vibration,1991,11(2):62~72.(in Chinese)
[5]Hoshiya M,Sutoh A.Kalman filter-finite element method in identification[J].Journal of Engineering Mechanics,ASCE,1993,119(2):197~210.
[6]Ghanem R,Shinozuka M.Structural system identification I:theory[J].Journal of Engineering Mechanics,ASCE,1995,121(2):255~264.
[7]Ghanem R,Shinozuka M.Structural system identification II:experimental verification[J].Journal of Engineering Mechanics,ASCE,1995,121(2):265~273.
[8]孙清,张陵,张爱社,伍晓红.基于扩展卡尔曼滤波(EKF)的结构动态物理参数识别[J].工程力学,2001,(增刊):224~230.Sun Qing,Zhang Ling,Zhang Aishe,Wu Xiaohong.Identification for structural dynamic physical parameters using Extended Kalman Filter(EKF)[J].Engineering Mechanics,2001,(Sup.):224~230.(in Chinese)
[9]Koh C G,See L M,Balendra T.Estimation of structural parameters in time domain:a substructure approach[J].Earthquake Eng.and Struc.Dynamics,1991,20:787~801.
[10]Oreta A C,Tanabe T.Localized identification of structures by Kalman filter[J].Structural Eng./Earthquake Eng.,1993,9(4):217~225.
[11]Oreta A C,Tanabe T.Element identification of member properties of framed structures[J].Journal of Structural Engineering,1994,120(7):1961~1976.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心