Nonlinear Structural Finite Element Model Updating Using Stochastic Filtering

Abstract

This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification purposes. The unscented Kalman filter (UKF) is used as parameter estimation technique to identify the unknown time-invariant parameters of the FE model. A two-dimensional, 3-bay, 3-story steel moment frame is used to verify the proposed framework. The steel frame is modeled using fiber-section beam-column elements with distributed plasticity and is subjected to a ground motion recorded during the 1989 Loma Prieta earthquake. The results show that the proposed methodology provides accurate estimates of the unknown material parameters of the nonlinear FE model.