A hybrid random-forest-based subset simulation (RFSS) method for probabilistic assessment of scour around pile groups under waves is proposed. In the RFSS, a random forest is used to replace the true limit state function (LSF); it is updated based on design samples in the first and last levels of the subset simulation method. For modelling, 127 laboratory datasets collected from the literature were used. First, an existing equation for predicting the scour depth around piles was modified using a metaheuristic approach. The performance of the modified equation was compared with existing equations and models. The modified equation was found to be more accurate than the existing formulas and AI-based models. A probabilistic model based on the RFSS was then developed by considering the modified formula as the LSF of scour depth. Solving two numerical problems, one hydraulic engineering problem and one scour problem validated the robustness and accuracy of the structural reliability method. The results showed that the RFSS is a robust and efficient method for solving high-dimensional real-world problems. Furthermore, compared to the Monte Carlo simulation, the RFSS was able to estimate the reliability index with less computational cost and the same accuracy.

中文翻译：

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海洋结构桩可靠性分析的有效替代模型

提出了一种基于混合随机森林的子集模拟（RFSS）方法，用于波浪下群桩周围冲刷的概率评估。RFSS中使用随机森林来代替真实极限状态函数（LSF）；它是根据子集模拟方法第一级和最后一级的设计样本进行更新的。为了建模，使用了从文献中收集的 127 个实验室数据集。首先，使用元启发式方法修改了预测桩周围冲刷深度的现有方程。将修改后的方程的性能与现有方程和模型进行了比较。发现修改后的方程比现有公式和基于人工智能的模型更准确。然后将修改后的公式视为冲刷深度的LSF，建立了基于RFSS的概率模型。解决两个数值问题，一个水利工程问题和一个冲刷问题验证了结构可靠性方法的稳健性和准确性。结果表明，RFSS 是解决高维现实世界问题的稳健且有效的方法。此外，与蒙特卡罗模拟相比，RFSS能够以更少的计算成本和相同的精度来估计可靠性指标。