Surrogate-Assisted Inverse Identification of Equivalent Stiffness Parameters for Composite Resilient Floor Systems

This study proposes a surrogate-assisted framework to characterize system-level equivalent stiffness parameters of composite resilient floor subsystems from global resonance features, calibrated and assessed for consistency using actual building acoustic tests. Since intrinsic material characterization alone may not fully capture the contribution of resilient layers under complex in situ boundary conditions, a parametric finite element model utilizing a transversely isotropic formulation with five independent stiffness-related parameters is coupled with Gaussian Process surrogates and a genetic algorithm. Repeated laboratory impact tests provide empirical bending and torsion resonance targets near 60 Hz and 155–160 Hz. The trained surrogates predict these targets with a root mean square error (RMSE) below 0.02, enabling the inverse estimation to identify representative parameter sets within a ±5% tolerance. The optimization yields a finite admissible parameter region, reflecting the inherent non-uniqueness of the inverse problem. To establish framework consistency, the identified equivalent parameters are integrated into a hybrid vibro-acoustic scheme and assessed for consistency against sound pressure level measurements from real-world building tests. Crucially, the sensitivity analysis indicates a mechanical divergence: in-plane stiffness primarily governs resonance-frequency reproduction, whereas out-of-plane stiffness is more influential in the noise-related structural wall-response-energy assessment within the admissible solution region. This behavioral mismatch demonstrates resonance matching alone is insufficient for design prioritization. The proposed framework establishes a configuration-level tool to systematically evaluate structural improvement directions without direct intrinsic material testing.