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| 激励与转角信息未知条件下桥梁结构物理参数识别 |
| Identification on physical parameters of bridge structures withunknown excitation and rotational response |
| 投稿时间：2023-03-15 修订日期：2023-04-21 |
| DOI： |
| 中文关键词: 物理参数识别 激励和转角信息未知 改进扩展卡尔曼滤波 静力凝聚法 量测矩阵分解 |
| 英文关键词:Physical parameter identification unknown excitation and rotational angle information improved extended Kalman filtering static coalescence method decomposition of the measurement matrix |
| 基金项目:天津市交通运输科技发展计划(2021-29、2022-48)；桥梁结构安全技术国家工程实验室开放课题(2019-QKF02) |
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| 中文摘要: |
| 桥梁结构物理参数识别往往需要完整的结构响应和已知的外加激励信息，这在实践中通常难以实现。为实现转角信息与外加激励数值均未知情况下的桥梁结构物理参数在线识别，先采用静力凝聚方法消去转动自由度，据此将扩展卡尔曼滤波方法与最小二乘方法结合，提出一种改进的扩展卡尔曼滤波方法。其中，针对静力凝聚后观测方程呈现高度非线性，导致卡尔曼量测矩阵计算低效的问题，引入基于克朗尼克积的分解算法。以一座简支梁桥为例，在移动荷载下进行了仿真测试。结果表明，所提方法能够准确地识别结构物理参数和外加激励，且克朗尼克积被引入后，识别精度与程序运行速度均有显著提升。 |
| 英文摘要: |
| The physical parameter identification of bridge structures often requires complete structural response and known extended excitation information, which is usually difficult to achieve in practice. In order to realize the online identification of the physical parameters of the bridge structure when the rotation information and the applied excitation values are unknown, an improved extended Kalman filter method is proposed by combining the extended Kalman filter method with the least square method after eliminating the rotational degrees of freedom by the static coalescence method.The decomposition algorithm based on the Kronecker product is introduced for the problem that the observation equation after static coalescence presents a high degree of nonlinearity, which leads to inefficient calculation of the Kalman measurement matrix. A simply supported girder bridge is simulated under moving load as an example. The results show that the proposed method can accurately identify the physical parameters of the structure and the applied excitation, and the accuracy of the identification and the running speed of the program are significantly improved after the introduction of the Kronecker product. |
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