Time history response prediction of stochastic SDOF structures through Kriging-NARX modeling and adaptive real-time hybrid simulation

Abstract

Emulating time history response is a critical benchmark for evaluating the seismic resilience of structures and assessing their reliability in the face of uncertainties. Traditional computational simulations, while commonly favored, often fall short in accurately describing the behavior of intricate structural components. Real-time hybrid simulation (RTHS) offers an effective and efficient experimental technique for replicating the responses of deterministic systems in a cyber-physical manner but encounters challenges when dealing with stochastic systems. This study proposes an innovative application of RTHS for time history response prediction of nonlinear stochastic structures. A cross-validation (CV)-Voronoi sampling strategy is applied to sequentially select uncertain parameter samples for RTHS. Nonlinear autoregressive with exogenous input (NARX) model is identified and updated from RTHS results in a sequential manner. Coefficients of final NARX model are surrogated using the Kriging meta-models to account for given uncertainties. Laboratory tests on single-degree-of-freedom (SDOF) structures with self-centering viscous dampers (SC-VD) were conducted to demonstrate the effectiveness of the proposed approach. Predicted time history responses from the final Kriging-NARX model are further compared and validated. It is demonstrated that the proposed approach provides an effective and efficient method for time history response prediction of stochastic systems especially when accurate numerical models are not available.