桥梁极值应力的改进高斯混合粒子滤波器动态预测  

Improved Gaussian Mixed Particle Filter Dynamic Prediction of Bridge Monitored Extreme Stress

作　　者：樊学平[1,2] 刘月飞[1,2] 吕大刚[3,4] 

机构地区：[1]兰州大学西部灾害与环境力学教育部重点实验室,甘肃兰州730000 [2]兰州大学土木工程与力学学院,甘肃兰州730000 [3]哈尔滨工业大学结构工程灾变与控制教育部重点实验室,黑龙江哈尔滨150090 [4]哈尔滨工业大学土木工程学院,黑龙江哈尔滨150090

出　　处：《同济大学学报（自然科学版）》2016年第11期1660-1666,共7页Journal of Tongji University:Natural Science

基　　金：国家自然科学基金(51608243);甘肃省自然科学基金(1606RJYA246);中央高校基本科研业务费专项资金(lzujbky-2015-300;lzujbky-2015-301)

摘　　要：为合理地动态预测在役桥梁的极值应力信息,应用桥梁健康监测(BHM)系统的长期日常监测极值应力数据,建立非线性动态模型,引入扩展卡尔曼滤波器(EKF)与高斯混合粒子滤波器(GMPF)相结合的改进高斯混合粒子滤波器(IGMPF)预测算法,对监测极值应力的一步向前预测分布参数及其状态变量的后验分布参数进行预测分析,并进行了实例验证.IGMPF不仅可以得到实测极值应力状态的合理重要性函数,还可以解决传统预测方法的短期性和精度不高的问题,为实际BHM系统的动力响应预测提供了理论基础.To reasonably and dynamically predict the extreme stress information of in-service bridge, in this paper, the nonlinear dynamic models were built including monitoring equation and state equation with the long-term everyday monitored extreme stress data of bridge health monitoring （BHM） system. Then the improved Gaussian mixed particle filter （IGMPF） prediction algorithm was introduced which was obtained by using extended Kalman filter （EKF） and GMPF. IGMPF can predict one-step forward prediction distribution parameters of monitored extreme stress and the posteriori distribution parameters of extreme stress state variable. Finally, an actual example was provided to illustrate the application and feasibility of the IGMPF algorithm built. The IGMPF prediction algorithm can not only obtain the reasonable importance functions of monitored extreme stress states, but also solve the problems of short-term prediction and low precision of the traditional prediction methods. It provides a theoretical foundation for dynamic response prediction of the actual BHM.

关 键 词：监测极值应力数据 非线性动态模型 扩展卡尔曼滤波器 高斯混合粒子滤波器 改进高斯混合粒子滤波器 

分 类 号：TU391[建筑科学—结构工程] TU392.5