Offline Real‐Time Hybrid Simulation Framework for Cable‐Damper Systems Based on Vector Form Intrinsic Finite Element Method

ABSTRACT Real‐time hybrid simulation (RTHS) provides an efficient experimental approach for evaluating the dynamic behavior of structural systems by partitioning them into numerical and experimental substructures (NS and ES) that interact in real time. Its offline variant, offline RTHS, relaxes strict real‐time constraints and reduces implementation complexity by exchanging complete time histories of boundary conditions and measured forces between the NS and ES and iteratively eliminating boundary incompatibilities, while retaining advantages of RTHS such as realistic loading paths and cost efficiency. This study proposes a vector form intrinsic finite element (VFIFE)‐based offline RTHS framework to enhance nonlinear response prediction for the NS, particularly under complex geometric nonlinearity. The framework is examined through virtual offline RTHS studies of a tuned mass damper (TMD)‐controlled 12‐story shear building subjected to seismic loading and a cable‐damper system under combined stochastic wind loading and axial support motion, highlighting its capability and computational efficiency for nonlinear NS analysis. A comprehensive parametric study reveals that the relaxation parameter governs the trade‐off between numerical stability and convergence efficiency, while the damping coefficient is the dominant parameter affecting iterative stability in offline RTHS for cable‐damper systems. Notably, the amplitude of axial support motion affects convergence efficiency non‐monotonically, arising from a transition from stochastic dynamic mismatch at intermediate amplitudes to deterministic synchronized resonance at higher excitation levels. Furthermore, external damping primarily elevates the critical threshold for parametric instability rather than suppressing post‐onset amplitudes. Collectively, this framework extends offline RTHS to nonlinear cable‐damper systems, offering guidelines for stable and efficient testing.