  Finite element model updating utilizing frequency response functions as
inputs is an important procedure in structural analysis, design and control.
This paper presents a highly efficient framework that is built upon Gaussian
process emulation to inversely identify model parameters through sampling. In
particular, a multi-response Gaussian process (MRGP) meta-modeling approach is
formulated that can accurately construct the error response surface, i.e., the
discrepancies between the frequency response predictions and actual
measurement. In order to reduce the computational cost of repeated finite
element simulations, an adaptive sampling strategy is established, where the
search of unknown parameters is guided by the response surface features.
Meanwhile, the information of previously sampled model parameters and the
corresponding errors is utilized as additional training data to refine the MRGP
meta-model. Two stochastic optimization techniques, i.e., particle swarm and
simulated annealing, are employed to train the MRGP meta-model for comparison.
Systematic case studies are conducted to examine the accuracy and robustness of
the new framework of model updating.
