Improved Particle Filter Prediction Algorithm of TimeVariant Reliability Indices for Bridges

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.

Key words: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