Experimental evaluation of CV-Voronoi based adaptive sampling for Kriging meta-modeling of multiple responses through real-time hybrid simulation

Real-time hybrid simulation (RTHS) integrates numerical modeling of analytical substructures with physical testing of experimental sub-structures thus enabling system global responses to be efficiently evaluated through component testing in size-limited laboratories. Traditional practice of RTHS focuses on responses evaluation of structures without considering their uncertainties. A cross-validation (CV)-Voronoi based adaptive sampling strategy is explored in this study for global meta-modeling of multiple response quantities of interests through RTHS for engineering systems with uncertainties. Based on the Kriging meta-model from initial samples, the CV-Voronoi based adaptive sampling sequentially identifies the sample points for RTHS tests in laboratory and observed responses of interests are used to update the Kriging meta-model. Multiple response distributions under structural uncertainties are thus acquired through limited number of experiments. RTHS tests of a two-degree-of-freedom system with self-centering viscous dampers (SC-VDs) are conducted in this study to experimentally evaluate the effectiveness of the CV-Voronoi based adaptive sampling for multiple response estimation. The accuracy of multi-response meta-models are further evaluated through comparison with validation tests. A stopping criterion is finally proposed for more efficient implementation of the adaptive sampling strategy. It is demonstrated that the CV-Voronoi based adaptive sampling strategy provides a viable technique to enable accurate global meta-modeling and estimation for multiple responses with limited number of RTHS tests in laboratory.