Non-unique Estimates in Material Parameter Identification of Nonlinear FE Models Governed by Multiaxial Material Models Using Unscented Kalman Filtering

Abstract

Bayesian nonlinear finite element (FE) model updating using input and output measurements have emerged as a powerful technique for structural health monitoring (SHM), and damage diagnosis and prognosis of complex civil engineering systems. The Bayesian approach to model updating is attractive because it provides a rigorous framework to account for and quantify modeling and parameter uncertainty. This paper employs the unscented Kalman filter (UKF), an advanced nonlinear Bayesian filtering method, to update, using noisy input and output measurement data, a nonlinear FE model governed by a multiaxial material constitutive law. Compared to uniaxial material constitutive models, multiaxial models are typically characterized by a larger number of material parameters, thus requiring parameter estimation to be performed in a higher dimensional space. In this work, the UKF is applied to a plane strain FE model of Pine Flat dam (a concrete gravity dam on King’s River near Fresno, California) to update the time-invariant material parameters of the cap plasticity model, a three-dimensional non-smooth multi-surface plasticity concrete model, used to represent plain concrete behavior. This study considers seismic input excitation and utilizes numerically simulated measurement response data. Estimates of the multi-axial material model parameters (for the single material model used in this study) are non-unique. All sets of parameter estimates yield very similar and accurate seismic response predictions of both measured and unmeasured response quantities.