Material Parameter Identification in Distributed Plasticity FE Models of Frame-Type Structures Using Nonlinear Stochastic Filtering

Abstract

This paper proposes a novel framework that combines high-fidelity mechanics-based nonlinear (hysteretic) finite-element (FE) models and a nonlinear stochastic filtering technique, referred to as the unscented Kalman filter, to estimate unknown material parameters in frame-type structures. The proposed identification framework updates nonlinear FE models using spatially limited noisy measurement data, and it can be further used for damage identification purposes. To validate its effectiveness, robustness, and accuracy, this framework is applied to a cantilever steel column representing a bridge pier and two-dimensional steel frame. Both structures are modeled using beam-column elements with distributed plasticity and are subjected to a suite of earthquake ground motions of varying intensity. The results indicate that the material parameters of the nonlinear FE models are accurately estimated provided that the loading intensity is sufficient to exercise the parts (branches) of the nonlinear material model, which are governed by the material parameters to be identified, and the measured response quantities are sufficiently sensitive to the material parameters to be identified, especially when a limited number of measurements are considered.