Active Learning for Probabilistic Safety Assessment of Adjacent Floating Bodies under Extreme Wave Loadings

Abstract Probabilistic safety assessment of adjacent floating bodies under extreme waves requires accurate estimation of structural failure probability, which is hampered by strongly nonlinear hydrodynamic coupling and prohibitive simulation costs. This study develops an active-learning framework that couples a high-fidelity computational fluid dynamics numerical wave tank with Gaussian-process surrogate modeling and wave-group parameterization, achieving converged failure-probability estimates with fewer simulations than direct Monte Carlo. Applied to a simplified two-body system under a 100-year return-period sea state, the framework reveals a nonmonotonic dependence of failure probability on the gap-to-breadth ratio G / D , with minimum risk at G / D = 0.5 and increasing risk at wider spacings due to progressive interbody phase decoupling. Sensitivity analyses across multiple failure thresholds confirm the robustness of this trend.