Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
Date
2022Language
en
Sujet
Résumé
Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an adaptive recursive Bayesian inference framework is developed to jointly estimate model parameters and the statistical characteristics of the prediction error that includes the effects of modeling error and measurement noise. The prediction error is usually modeled as a Gaussian white noise process in a Bayesian model updating framework. In this study, the prediction error is assumed to be a nonstationary Gaussian process with an unknown and time-variant mean vector and covariance matrix to be estimated. This allows one to better account for the effects of time-variant model uncertainties in the model updating process. The proposed approach is verified numerically using a 3-story 1-bay nonlinear steel moment frame excited by an earthquake. Comparison of the results with those obtained from a classical nonadaptive recursive Bayesian model updating method shows the efficacy of the proposed approach in the estimation of the prediction error statistics and model parameters. © 2021 American Society of Civil Engineers.
Collections
Related items
Showing items related by title, author, creator and subject.
-
Nonlinear Model Updating Using Recursive and Batch Bayesian Methods
Song M., Astroza R., Ebrahimian H., Moaveni B., Papadimitriou C. (2020)This paper studies the performance of recursive and batch Bayesian methods for nonlinear model updating. Unscented Kalman filter (UKF) is selected to represent the recursive Bayesian method, and two UKF approaches are ... -
Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters
Eftekhar Azam S., Mariani S., Attari N.K.A. (2017)In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures. The reduced-order model of a structure is ... -
Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures
Ebrahimian H., Astroza R., Conte J.P., Papadimitriou C. (2018)This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the ...