应用高斯粒子滤波器的桥梁可靠性在线预测  

On-line reliability prediction of bridges based on Gaussian particle filter

作　　者：樊学平[1,2] 刘月飞[1,2] 吕大刚[3] 

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

出　　处：《哈尔滨工业大学学报》2016年第6期164-169,共6页Journal of Harbin Institute of Technology

基　　金：国家自然科学基金面上项目(51178150);兰州大学中央高校基本科研业务费专项资金(lzujbky-2015-300;lzujbky-2015-301)

摘　　要：为采用实时监测信息对桥梁结构构件的可靠性进行动态预测分析,应用健康监测系统的长期大量监测数据,建立了基于监测数据的动态模型(监测方程与状态方程),引入混合高斯粒子滤波器(MGPF),基于粒子滤波方法、贝叶斯方法以及动态模型,对监测信息状态变量的后验分布参数和监测值的一步向前预测分布参数进行预测分析.混合高斯粒子滤波方法通过重抽样技术,提高了动态模型的预测精度.基于实时监测信息可以不断修正抽样粒子的权重,进而解决粒子退化问题.最后基于实时预测的分布参数,结合一次二阶矩(FOSM)方法,对桥梁结构构件的可靠性进行在线动态预测分析.To dynamically predict reliability of bridge members with real-time monitored information, with the long- term mass monitored data of health monitoring system, the data-based dynamic model including monitoring equation and state equation was built, and then the mixed Gaussian particle filter（MGPF） was introduced. With partiele filter method, Bayesian method and dynamic model, the posteriori distribution parameters of state variable and one- step forward prediction distribution parameters of monitored data were predicted. Through resampling technique, with MGPF, the prediction precision of dynamic model can be increased. Based on the real-time monitoring data, the weights of resampled particles can be constantly updated. Therefore, the problem of particle degradation is solved. Finally based on the real-time predicted distribution parameters, with the first order second moment （FOSM） method, the on-line and dynamic reliability of bridge members is predicted.

关 键 词：监测数据 动态模型 混合高斯粒子滤波器 贝叶斯方法 可靠性预测 

分 类 号：TU391[建筑科学—结构工程] TU392.5