An Application of Particle Filter for Parameter Estimation and Prediction in Geotechnical Engineering

Abstract

This chapter presents an application example of a nonlinear Kalman Filters (KFs), i.e., Particle Filter (PF), for state (or parameters) estimation and prediction of a dynamical system in geotechnical engineering. First key characteristics of dynamical systems in geotechnics, which need to be considered in filtering, are described by showing some figures, and why PF is necessary for geotechnical applications is explained. Then, a detailed algorithm and implementation of PF for geotechnical problems are presented with key equations. The PF is demonstrated through a case history focusing on deformation behavior of a ground due to embankment construction. The PF is applied to estimation of geotechnical parameters and predictions of future settlement behavior of the ground to discuss the effectiveness of the PF in geotechnical engineering. The results of the case history have shown that PF has presented great promise as an accurate parameter identification for a nonlinear dynamic model. The simulation with the identified parameters predicts the actual measurement data with high accuracy even though a limited amount of measurement data was used in identification stage. The PF provides more information on estimates than optimization methods because the estimates are obtained in the form of probability density functions (PDFs). This characteristic can contribute to risk analysis and reliability-based decision-making in geotechnical practice.