Bayesian State and Parameter Estimation using Particle Filters

The focus of this paper is to demonstrate the application of a recently developed Bayesian state estimation method to the recorded seismic response of a building. The method, known as the particle filter, is based on stochastic simulation. Unlike the well-known extended Kalman filter, it is applicable to highly nonlinear systems with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the particle filter simulations are also discussed. The particle filter is applied to strong motion data recorded in the 1994 Northridge earthquake in a 7-story hotel whose structural system consists of non-ductile reinforced-concrete moment frames, two of which were severely damaged during the earthquake. A simplified identification model is proposed: a time-varying nonlinear degradation model that is derived from a nonlinear finite-element model of the building previously developed at Caltech. For this case study, the particle filter provides consistent state and parameter estimates, in contrast to the extended Kalman filter, which provides inconsistent estimates