Data-driven Arbitrary Polynomial Chaos Expansion on Uncertainty Quantification for Real-time Hybrid Simulation Under Stochastic Ground Motions

Abstract

Uncertainties in real-time hybrid simulation include structural parameters and ground motion. Uncertain parameters often do not follow common distribution types. Data-driven arbitrary polynomial chaos constructs optimal orthogonal polynomial basis based on the sample data without distribution assumption. In this study, the data-driven polynomial chaos is compared with other generalized polynomial chaos from the aspects of the rate of error convergence when applied for uncertainty quantification of real-time hybrid simulation. Moreover, uncertainties of ground motion are considered in the RTHS problem to represent the scenarios with more complex input variables. Different statistical indicators are utilized to evaluate the accuracy of the alternative model in comparison with the Monte Carlo simulation results. Compared with generalized polynomial chaos, the data-driven arbitrary polynomial chaos presents potential for uncertainty quantification of real-time hybrid simulation with approximate or better accuracy. Actuator delay in RTHS could change the sensitivity of model output to the random variables.