A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics - Nabiyan, Mansureh-Sadat; Sharifi, Mahdi; Ebrahimian, Hamed; Moaveni, Babak (Frontiers in Built Environment, 2023)
Dynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decrease significantly when the pre-assumed statistical characteristics of the model prediction error are violated. To overcome this issue, this paper presents an adaptive recursive variational Bayesian approach to estimate statistical characteristics of the prediction error jointly with the unknown model parameters. This approach improves the accuracy and robustness of model updating process by including the estimation of model prediction error. The performance of this approach is demonstrated using numerically simulated data obtained from a structural frame with material nonlinearity under earthquake excitation. Results show that in the presence of non-stationary noise/error, the non-adaptive approach fails to estimate unknown model parameters, whereas the proposed approach can accurately estimate them.