The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.

中文翻译：

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使用 ANN-BN 推理的浮动结构系泊缆的可靠性分析

恶劣的海洋环境是对浮式结构系统安全的重大威胁。为了解决这个问题，系泊系统已被广泛应用为稳定浮动结构的重要组成部分。本文提出了一种基于人工神经网络-贝叶斯网络推断在给定极端环境条件下评估系泊线可靠性的方法。采用不同类型的人工神经网络，包括径向基函数神经网络和反向传播神经网络，根据一系列实测环境数据预测系泊缆的极端响应。然后根据系泊系统的失效准则建立极端海况下的失效数据库。当最大拉力超过允许的断裂强度时，系泊缆出现故障。最后，利用贝叶斯网络进行了不同载荷方向下系泊浮式结构的可靠性分析。为了证明所提出的方法，估计了水深为 1500 m 的样本半潜式平台的故障概率。该方法利用人工神经网络的计算效率能力，并验证人工神经网络对浮动结构的响应预测。此外，它还可以用于估计其他复杂浮动结构的失效概率。为了证明所提出的方法，估计了水深为 1500 m 的样本半潜式平台的故障概率。该方法利用人工神经网络的计算效率能力，并验证人工神经网络对浮动结构的响应预测。此外，它还可以用于估计其他复杂浮动结构的失效概率。为了证明所提出的方法，估计了水深为 1500 m 的样本半潜式平台的故障概率。该方法利用人工神经网络的计算效率能力，并验证人工神经网络对浮动结构的响应预测。此外，它还可以用于估计其他复杂浮动结构的失效概率。