基于折扣高斯粒子滤波器的桥梁可靠性动态预测

桥梁健康监测(BHM)系统在长期运营中积累了大量信息,如何利用这些信息动态预测结构可靠性已成为BHM领域的关键科学问题之一.为合理预测桥梁的动态可靠性,应用BHM系统日常监测的极值应力数据,建立带有最优折扣因子的动态线性模型,结合高斯粒子滤波器给出折扣高斯粒子滤波器预测算法,分别对日常监测极值应力的一步向前预测分布参数和状态变量后验分布参数进行修正预测,并基于此,采用一次二阶矩(FOSM)方法预测桥梁的动态可靠性,结合桥梁实测数据对所提方法进行了验证分析,为桥梁预防性养护维修决策提供理论基础.

Dynamic Reliability Prediction of Bridges Based on Gaussian Particle Filter with Discount Factors

Bridge health monitoring (BHM) system produces a huge amount of monitored data in the long-term service periods, the properly handling of the distantly provided information for dynamically predicting structural reliability is one of the main difficulties in the BHM field. To reasonably predict time-variant reliability of in-service bridge, this paper firstly builds the linear dynamic models (monitoring equation and state equation) with the optimum discount factors based on the long-term everyday monitored extreme stress data of BHM system. Then, the one-step forward prediction distribution parameters of monitored extreme stress and the posteriori distribution parameters of state variable are respectively predicted by Gaussian particle filter prediction algorithm with discount factors. Finally, the dynamic reliability indices of bridge are predicted with first order second moment (FOSM) method, and the monitored data of an actual bridge is provided to illustrate the application and feasibility of the proposed method, which can provide the theoretical foundation for preventive maintenance decision of the actual bridge.