Journal of Structural Engineering · 2022 · 18 citations · 54 references
EngineeringCivil EngineeringEarthquake EngineeringFinite Element AnalysisStructural DynamicsDynamic Response AnalysisEarthquake Response Of StructuresNonlinear Structural AnalysisSimulation MethodsStructural Response AnalysisStructural SimulationFinite Element SimulationGround Motion ModelingPolynomial Chaos SurrogatesSeismic ModelingSeismic ResponseStochastic Ground Motion SimulationStructural Dynamics ModelingStructural Seismic Response
Real-time hybrid simulation (RTHS) provides an efficient and effective experimental technique for rate-dependent energy-dissipation devices in seismic hazard mitigation. The structure under investigation is generally divided into analytical and physical substructures to enable large-scale experiments for system behavior. Accurate modeling of analytical substructures is critical for truthful structural response replication through RTHS. This presents challenges to laboratory practice of RHTS such as capability of specialized finite-element software to replicate complex nonlinear behavior, and the equipment capacity to accommodate large-scale finite-element modeling to be executed in a real-time manner. This study explores the use of a polynomial chaos nonlinear autoregressive with exogenous input (PC-NARX) model to conduct RTHS in laboratories using existing equipment and general-purpose finite-element analysis (FEA) software readily available in earthquake engineering research. The NARX model can be trained using any existing FEA software for a good representation of structural dynamics. Polynomial chaos expansion (PCE) is then introduced to surrogate NARX model coefficients to account for ground motion uncertainties. Laboratory tests of a self-centering viscous damper were conducted as proof of concept to experimentally demonstrate the effectiveness of RTHS with PC-NARX metamodeling approach. The results were further compared with the kriging surrogate technique for NARX model coefficients to explore a better technique to account for uncertainties in RTHS.
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