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. •An adaptive meta-model has been implemented into a model updating formulation.•Kriging estimates are constructed by reduced-order models.•The approach is used for updating finite element models using simulated response data.•Computational effort is reduced to manageable levels.•Numerical efficiency is achieved without compromising the accuracy of the results.