Accurate fatigue assessment is a challenging and crucial aspect of riser design. The prediction of the long-term fatigue damage must account for numerous sea states of different wave heights, periods, and directions. Each sea state entails a dynamic analysis, often performed in the time domain owing to the significant nonlinearities. Because of the short-term uncertainties from irregular waves, the simulation duration must be sufficiently long for results to converge. To alleviate the hefty computational cost of long-term fatigue analysis, researchers have proposed efficient methods, but these are not without drawbacks; in particular, wave directionality is commonly neglected. This paper presents an efficient method for long-term fatigue analysis based on time domain simulation, considering wave directionality among other things. The proposed method is based on an enhanced version of control variates to reduce the variance in Monte Carlo simulations (MCS). The control function is constructed by training artificial neural network (ANN) models using existing MCS data. Here, a customized scheme is developed to allow for the situation that the training data and ANN prediction cases have different wave directions. The proposed method is unbiased and provides an error estimate. Simulations are performed on a floating system, and the proposed method is found to improve the efficiency of MCS significantly. Different scenarios such as fixed and random wave directions are compared, confirming that wave directionality is critical and should be included in a long-term fatigue assessment.

准确的疲劳评估是立管设计中具有挑战性和至关重要的方面。长期疲劳损伤的预测必须考虑到不同波高，周期和方向的众多海况。每个海洋状态都需要进行动态分析，由于存在明显的非线性，通常会在时域中进行。由于不规则波的短期不确定性，模拟持续时间必须足够长才能收敛。为了减轻长期疲劳分析的繁重计算成本，研究人员提出了有效的方法，但是这些方法也不是没有缺点。特别是，通常忽略了波的指向性。本文提出了一种基于时域模拟的长期疲劳分析的有效方法，其中考虑了波的方向性。所提出的方法基于控制变量的增强版本，以减少蒙特卡洛模拟（MCS）中的方差。通过使用现有MCS数据训练人工神经网络（ANN）模型来构建控制功能。在这里，开发了一种定制的方案以允许训练数据和ANN预测情况具有不同的波向的情况。所提出的方法是无偏的，并提供了误差估计。在浮动系统上进行了仿真，发现所提出的方法可以显着提高MCS的效率。比较了固定和随机波浪方向等不同情况，确认了波浪方向性至关重要，应将其包括在长期疲劳评估中。