Surrogate Based Stochastic Hybrid Simulation

Hybrid simulation(HS)has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size limited laboratories.The critical and/or complex components of a structural system are tested as experimental substructures in laboratories while the rest is analytically modeled as numerical substructures.Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment.Reliable structural performance evaluation therefore calls for stochastic hybrid simulation(SHS)to explicitly account for substructure uncertainties.Experimental design of SHS is explored in this study to account for uncertainties within analytical substructures.Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for experimental design of SHS.Meta-modeling through polynomial chaos expansion(PCE)is established from computational simulation of a nonlinear single-degree-of-freedom(SDOF)structure to evaluate the influence of nonlinear behavior and ground motions uncertainties.A series of hybrid simulation are further conducted in the laboratory to validate the findings from computational analysis.It is shown that Sobol sequence provides a good starting point for experimental design of stochastic hybrid simulation.Nonlinear structural behavior involving stiffness and strength degradation however could significantly increase the number of hybrid simulations to acquire accurate statistic estimation for structural response of interest.Compared with the statistical moments calculated directly from hybrid simulations in the laboratory,the meta-model through PCE gives more accurate estimation therefore providing a more effective way for uncertainty quantification.