基于广义卡尔曼滤波的桥梁结构物理参数识别的子结构法  

Proposing a Substructure Method of Extended Kalman Filter(EKF) for Identifying Physical Parameters of Bridge Structure

作　　者：吴子燕[1] 丁兰[1] 刘书奎[1] 

机构地区：[1]西北工业大学力学与土木建筑学院,陕西西安710072

出　　处：《西北工业大学学报》2010年第3期425-428,共4页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金(50878184);西北工业大学研究生创业种子基金(Z200929)资助

摘　　要：文章将子结构法与广义卡尔曼滤波(EKF)算法相结合,推导出基于子结构有限元模型的状态方程和观测方程,对观测方程和状态方程组成的非线性参数系统应用广义卡尔曼滤波,成功识别出子结构的物理参数。该方法可以根据需要建立不同位置的子结构动力方程进行特定位置、并进一步实现整体结构的物理参数识别,为测量响应信息不完备条件下桥梁结构物理参数识别问题提供了一个较好的解决方法。数值算例表明,所提方法识别精度高、收敛速度快、抗噪性能好,适用于大型桥梁结构物理参数识别。Aim. The traditional EKF method requires that all the response information be measured, which may not be the ease for many bridge structures. Hence we propose our substructure method, which we explain in sections 1 and 2 of the full paper. Their core is： ＂Firstly, we select the substructure with complete response information as the research object. Secondly, the ohservation equation and the state equatiort hased on finite deraent model are derived by the motion equations of the substructure. Applying the EKF method to the nonlinear parametric system consisting of the state equation and the observation equation, we identify the physical parameters of the substructure. Combined with EKF, the substructure method proposed in this paper offers an efficient approach for the nonlinear parametric system identification problem with incomplete response information. ＂ The simulation results, given in Table 2 and Fig. 3, demonstrate preliminarily that the proposed approach is capable of identifying the physical parameters with rapidity and high precision.

关 键 词：桥梁结构 参数识别 广义卡尔曼滤波 子结构 

分 类 号：U448.34[建筑科学—桥梁与隧道工程] TB114.2[交通运输工程—道路与铁道工程]