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桥梁时变可靠度指标的改进粒子滤波预测算法
引用本文:樊学平,屈广,刘月飞.桥梁时变可靠度指标的改进粒子滤波预测算法[J].同济大学学报(自然科学版),2019,47(8):1115-1122.
作者姓名:樊学平  屈广  刘月飞
作者单位:兰州大学 土木工程与力学学院, 甘肃 兰州 7300001;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,兰州大学 土木工程与力学学院, 甘肃 兰州 7300001,兰州大学 土木工程与力学学院, 甘肃 兰州 7300001;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
基金项目:国家自然科学基金(51608243)
摘    要:基于健康监测时间序列数据,提出了桥梁动态可靠度指标的改进粒子滤波预测方法.首先,利用监测极值数据建立动态模型,将其作为粒子滤波算法的状态方程和监测方程;然后,采用贝叶斯动态线性模型(BDLM)为粒子滤波器提供随时间更新的动态建议分布,以解决传统粒子滤波算法的样本退化问题,同时增加了粒子滤波算法的鲁棒性及自适应性;进而利用改进的粒子滤波算法(IPF),结合极值监测数据实现结构极值的动态预测,并结合一次二阶矩(FOSM)可靠性方法,实现桥梁结构可靠度指标的动态预测;最后通过在役桥梁工程实例与设计试验对所提模型和方法的合理性与有效性进行验证.

关 键 词:桥梁健康监测数据  动态模型  建议分布函数  改进粒子滤波算法  一次二阶矩方法  动态可靠度指标预测
收稿时间:2018/4/29 0:00:00
修稿时间:2019/5/21 0:00:00

Improved Particle Filter Prediction Algorithm of Time Variant Reliability Indices for Bridges
FAN Xueping,QU Guang and LIU Yuefei.Improved Particle Filter Prediction Algorithm of Time Variant Reliability Indices for Bridges[J].Journal of Tongji University(Natural Science),2019,47(8):1115-1122.
Authors:FAN Xueping  QU Guang and LIU Yuefei
Institution:School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Mechanics on Disaster and Environment in Western China of the Ministry of Education, Lanzhou University, Lanzhou 730000, China,School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China and School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Mechanics on Disaster and Environment in Western China of the Ministry of Education, Lanzhou University, Lanzhou 730000, China
Abstract:This paper proposes an improved particle filter (IPF) prediction approach of dynamic reliability indices for bridges based on monitoring time series data. First, the dynamic models, which can provide state equation and monitoring equation for the IPF, are built with the monitoring extreme data of bridges. Next, the Bayesian dynamic linear model (BDLM) is utilized to produce the real time updated proposal distribution for IPF in order to solve the sample degradation problem and increase the robustness and adaptability of the traditional particle filter. After that, by using the IPF approach, the structural extreme information is dynamically predicted based on the monitoring extreme data, and dynamic reliability indices of bridges are predicted by using the first order second moment (FOSM) reliability method. Finally, three existing bridges and a designed experiment are provided to illustrate the feasibility and application of the proposed model and method.
Keywords:bridge health monitoring data  dynamic models  proposal distribution function  improved particle filter(IPF) prediction method  first order second moment(FOSM) method  dynamic reliability indices prediction
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