Implementation of an adaptive meta-model for Bayesian finite element model updating in time domain

This work explores the feasibility of integrating an adaptive meta-model into a finite element model updating formulation using dynamic response data. A Bayesian model updating approach based on a stochastic simulation method is considered in the present formulation. Such approach is combined with a surrogate technique and an efficient model reduction technique. In particular, an adaptive surrogate model based on kriging interpolants and a model reduction technique based on substructure coupling are implemented. The integration of these techniques into the updating process reduces the computational effort to manageable levels allowing the solution of complex problems. The effectiveness of the proposed strategy is demonstrated with three finite element model updating applications.