For an improved estimation of marine structural reliability, a consideration of random variable dependency is essential. With a limited study on dependence modelling of marine structures, this study proposes a framework for reliability assessment of ocean structural systems with multidimensional variables. This framework captures possible nonlinearity and tail dependence in the variables using vine copula. The proposed method develops a graphical structure of random variables consisting of nodes, edges, and trees using the Drawable Vine (D-vine) approach. This study demonstrates the developed framework on a jacket support structure subjected to the extreme environmental load conditions at Jeanne D′ Arc basin on Canada's east coast. The structure's reliability is evaluated with optimally selected copulas in the D-vine trees and associated marginal distributions. A comparison between the reliability result using the D-vine copula method, Gaussian coupling assumption, and statistical independence between variables proved its superiority in modelling variable dependence of complex marine systems. The probability of failure ($P_f)$ using D-vine copula was closer to the reference Importance Sampling (IS) results than other methods.

为了更好地估计海洋结构的可靠性，必须考虑随机变量的依赖性。由于对海洋结构的依赖性建模的研究有限，本研究提出了一个具有多维变量的海洋结构系统可靠性评估的框架。该框架使用藤蔓copula捕获变量中可能存在的非线性和尾部相关性。所提出的方法使用Drawable Vine（D-vine）方法开发了由节点，边和树组成的随机变量的图形结构。这项研究表明，在加拿大东海岸的珍妮·D·阿克盆地，在极端环境载荷条件下，夹克支撑结构的框架已经开发完毕。结构' 通过在D-蔓生树木中选择最佳的copula以及相关的边际分布来评估其可靠性。使用D-vine copula方法的可靠性结果，高斯耦合假设和变量之间的统计独立性之间的比较证明了其在建模复杂海洋系统变量依赖性方面的优势。失败的可能性（$P_F）$ 使用D-vine copula比其他方法更接近参考重要性抽样（IS）结果。