Data assimilation is a method for improving prediction by using observation data to reduce the uncertainty associated with the prediction. Particle filter (PF) is one of data assimilation methods based on Bayesian inference and Monte Carlo approach for inverse problems. In this study, we combine the PF with reliability analysis to update limit state probabilities by time series observations. The proposed method is demonstrated through a consolidation settlement problem. The settlement is predicted by using soil/water coupled FEM analysis. Model parameters of the FEM and the predicted settlement are updated by synthesized observation data of settlements. Probability distributions of them are expressed by many sample realizations, which are called particles in PF algorithm. The exceedance probabilities for a specified settlement criterion at observed and unobserved locations are also updated based on time-series observation data. It is shown that the predicted settlement and the conditional limit state probability can be updated quantitatively according to the increase of observation data, which leads to reasonable decision-making.