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

Abstract

AbstractReal‐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.