Bayesian RC-Frame Finite Element Model Updating and Damage Estimation Using Nested Sampling with Nonlinear Time History

Abstract

This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are selected through nested sampling, and their probability density is estimated using nonlinear time history. In the first step, we estimate the error standard deviation and select the FEM parameters that are required to be updated by FEMU. In the second step, we estimate the probability density of the selected parameters and realize the FEMU through the resampling method and kernel density estimation (KDE). Additionally, we propose a damage state estimate approach, which is a derivative method of the FEMU sample. The numerical results demonstrate that the proposed approach is reliable for the Bayesian FEMU and damage state estimation using nonlinear time history.