To design an engineering system, testing in extreme conditions is at least recommended if not required. There are ambiguities about how to define an extreme state and how to consider it in the design of a system or its operation. The probability estimation of such an event is challenging due to data scarcity, especially in many engineering domains, e.g. offshore development. In this study, available techniques for analyzing the probability of extreme events are examined for their suitability in engineering applications, and a framework is proposed for rare event risk analysis. The framework is comprised of three phases. In the first phase, the outlier based extreme value theory is implemented to estimate the rare event probability. The maximum likelihood criterion is used to estimate the extreme distribution parameters. In the second phase, the rare event is considered as a heavy tail event, and the tail index is estimated through the Hill and the SmooHill estimator. In the third phase, The uncertainty analysis is conducted, and the risk is computed. The proposed methodology is tested for extreme iceberg risk assessment on large offshore structures in the Flemish Pass basin. For this specific case, the estimated design extreme iceberg speed was 4.31 km/h, with an occurrence probability of 3.61E-06.

为了设计工程系统，如果不需要，至少建议在极端条件下进行测试。关于如何定义极端状态以及如何在系统设计或其操作中考虑极端状态存在歧义。由于数据稀缺，尤其是在许多工程领域（例如，海上开发）中，此类事件的概率估计具有挑战性。在这项研究中，检查了用于分析极端事件概率的可用技术，以探讨其在工程应用中的适用性，并提出了一个用于罕见事件风险分析的框架。该框架包括三个阶段。在第一阶段，实施基于异常值的极值理论来估计稀有事件概率。最大似然准则用于估计极端分布参数。在第二阶段 罕见事件被认为是重尾事件，并且通过Hill和SmooHill估计器估计尾索引。在第三阶段，进行不确定性分析，并计算风险。对该方法进行了测试，以评估佛兰德关口盆地大型海上结构的极端冰山风险。对于此特定情况，估计的设计极端冰山速度为4.31 km / h，发生概率为3.61E-06。