Real‐time hybrid simulation with multi‐fidelity Co‐Kriging for global response prediction under structural uncertainties

Abstract

Real‐time hybrid simulation (RTHS) provides an effective and efficient experimental technique to enable large‐ or full‐scale experiments to account for rate‐dependent behavior in size limited laboratories. Traditional practice of RTHS assumes deterministic substructure properties therefore could not account for structural uncertainties in global response prediction. This study explores the use of Co‐Kriging metamodeling for global response prediction under the presence of structural uncertainties. RTHS in laboratory is used as high‐fidelity (HF) modeling while computational simulation of the prototype structure under investigation is used as low‐fidelity (LF) modeling. Multifidelity modeling is integrated through Co‐Kriging to render accurate response prediction over the entire sample space of structural uncertainties. An entropy‐based adaptive strategy is used to sequentially determine the sampling points for HF RTHS and LF simulation. The proposed multifidelity Co‐Kriging approach is experimentally evaluated through RTHS of a single‐degree‐of‐freedom structure with a self‐centering viscous damper. The Co‐Kriging predicted maximum structural responses are further compared with validation tests. It is demonstrated that the Co‐Kriging can effectively reduce the number of RTHS tests in laboratory and significantly improve the metamodel accuracy for global prediction of structural response under uncertainties. The presented study presents an innovative way to further expand the capacity of RTHS for uncertainty quantification toward seismic hazard mitigation.

Development of an explicit time integration algorithm based on optimal integration parameter updating

“…In recent years, special emphasis has been placed in the improvement of explicit algorithms. [39][40][41][42][43] Nevertheless, all these current methods have their inherent limitations in applicability. For example, the Newmark explicit method 1 may fail to convergence in high order modes.…”

“…In recent years, special emphasis has been placed in the improvement of explicit algorithms 39–43 . Nevertheless, all these current methods have their inherent limitations in applicability.…”

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

“…The SC‐VD device is considered an experimental substructure and the rest is taken as analytical substructure. Chen et al 36 integrated the Co‐Kriging technique with real‐time hybrid simulation to quantify the effect of structural uncertainty on the same SDOF structure. The newly developed SC‐VD device is composed of preloaded ring springs for SC ability and a fluidic VD for energy dissipation 41 .…”

“…Yu et al 35 applied the CV‐V sampling method to multiresponse RTHS and used the validation test to test the accuracy of the meta‐model. Chen et al 36 further used multifidelity Co‐Kriging as a global meta‐model for hybrid simulation experiments, which achieved accurate predictions with fewer samples. By obtaining the real structural response through hybrid simulation and quantifying the effect of structural uncertainty by Kriging model, the key issues of prohibitive cost of acquiring actual structural responses through large number of laboratory experiments were solved.…”

Integrating real‐time hybrid simulation with multi‐fidelity Monte Carlo predictor for seismic fragility assessment

“…Results from HF and LF models have been explored by researchers to construct multi-level surrogate models for response prediction such as Co-Kriging [35][36] and hierarchical Kriging. 37 Chen et al 38 considered RTHS tests in laboratory as HF model and approximate model of the prototype structure under investigation is used as LF model. Multi-fidelity modeling is integrated through Co-Kriging to render accurate response prediction over the entire sample space of uncertainties.…”