A new time-domain probabilistic technique based on hierarchical Bayesian modeling (HBM) framework is proposed for calibration and uncertainty quantification of hysteretic type nonlinearities dynamical systems. Specifically, hyper models are introduced respectively material model parameters as well prediction error variance parameters, aiming to consider both the due unmodeled dynamics. asymptot...

Uncertainty induced by our incomplete state of knowledge about engineering systems and their surrounding environment give rise to challenging problems in the process of building predictive models for the system behavior. One such challenge is the model selection problem, which arises due to the existence of invariably multiple candidate models with different mathematical forms to represent the ...

A mathematical model of chicken processing that quantitatively describes the transmission of Campylobacter on chicken carcasses from slaughter to chicken meat product has been developed in Nauta et al. (2005). This model was quantified with expert judgment. Recent availability of data allows updating parameters of the model to better describe processes observed in slaughterhouses. We propose Ba...

In FE model updating, as in any identification procedure, we select some parameters in the model, and try to fine-tune them to minimize the discrepancy between the model predictions and the measured data. The paper compares the performance of the generic element matrices, recently introduced by the authors, with other selection strategies for finite element model updating. The updated models ob...