Wave impact loads on maritime structures can cause casualties, damage, pollution and operational delays. Consequently, their extreme values should be accounted for in the design of these structures. However, this is challenging, as wave impact events are both rare and highly complex, requiring both high-fidelity simulations and long analysis durations to reliably quantify the associated design loads. Moreover, existing extreme value prediction methods are neither specifically developed nor adequately validated for wave impact phenomena. We therefore introduce the new Probabilistic Adaptive Screening (PAS) method for predicting extreme non-linear loads on maritime structures. The method integrates copula-based statistical dependence modelling with multi-fidelity screening and adaptive sampling. This framework enables efficient extreme value prediction by statistically mapping low-fidelity indicator variables to high-fidelity impact loads. The method allows for efficient linear potential flow indicators to be used in the low-fidelity stage, even for strongly non-linear cases. Its statistical framework is validated against four non-linear test cases, including non-linear waves, ship vertical bending moments, green water impact loads, and slamming loads. It is concluded that PAS with optimal settings accurately estimates both the short-term distributions and extreme values in these test cases, with most probable maximum (MPM) values within 2-15% of the reference brute-force Monte-Carlo Simulation (MCS) results. In addition, PAS achieves this performance very efficiently, requiring in the order of 1-3% of the high-fidelity simulation time needed for conventional MCS. These results demonstrate that PAS can reliably reproduce the statistics of both weakly and strongly non-linear extreme load problems, while significantly reducing the associated computational cost compared to MCS.

翻译：波浪对海洋结构的冲击荷载可能导致人员伤亡、结构损坏、环境污染与作业延误。因此，在结构设计中必须考虑其极端值。然而，由于波浪冲击事件具有罕见性与高度复杂性，需要高精度模拟与长时间分析才能可靠量化相关设计荷载，这使得预测工作极具挑战。此外，现有极端值预测方法并非专门针对波浪冲击现象开发，也缺乏充分验证。为此，我们提出一种新的概率自适应筛选（PAS）方法，用于预测海洋结构的极端非线性荷载。该方法将基于Copula的统计依赖建模与多精度筛选及自适应采样相结合，通过将低精度指标变量统计映射至高精度冲击荷载，实现高效的极端值预测。即使在强非线性情况下，该方法也允许在低精度阶段使用高效的线性势流指标。其统计框架在四个非线性测试案例中得到验证，包括非线性波浪、船舶垂向弯矩、甲板上浪冲击荷载与砰击荷载。结果表明：采用最优设置的PAS方法能准确估计这些案例的短期分布与极端值，其最可能最大值（MPM）与参考暴力蒙特卡洛模拟（MCS）结果的偏差在2-15%以内。此外，PAS以极高效率实现此性能，所需的高精度模拟时间仅为传统MCS的1-3%。这些结果证明，PAS能够可靠复现弱非线性与强非线性极端荷载问题的统计特性，同时相比MCS显著降低计算成本。