中文摘要：

就在波浪高度，风速度，和当前的速度之中的依赖关系而言，我们经由阿基米得的性交功能构造新奇 trivariate 关节概率分布。在 Bohai 海的波浪高度，风速度，和当前的速度的全部的 30 年的数据是 hindcast 并且为案例研究取样。也就是， Gumbel 分发， lognormal 分发， Weibull 分发，和皮尔森打的四种分布 III 分发，是为波浪高度，风速度，和当前的速度的边缘的分布的候选人模型。皮尔森类型 III 分发作为最佳的模型被选择。这些环境条件的 Bivariate 和 trivariate 概率分布基于四 bivariate 和 trivariate 被建立阿基米得的性交，也就是 Clayton，弗兰克， Gumbel-Hougaard，和 Ali-Mikhail-Haq 性交。这些联合概率模型能在三个变量之中最大化边缘的信息和依赖。这三个变量的设计返回值能被三个方法获得：univariate 概率，有条件的概率，和联合概率。不同负担联合的联合回来时期被建议模型估计。站台回答(包括底砍，翻的时刻，和甲板排水量) 进一步被计算。为一样的回来时期，设计有条件、联合的概率模型获得的波浪高度，风速度，和当前的速度珍视是比由 univariate 概率的那些小得多的。就在变量之中的依赖而言， multivariate 概率分布为海洋平台设计提供靠近的设计参数给实际的海状态。

英文摘要：

Considering the dependent relationship among wave height, wind speed, and current velocity, we construct novel trivariate joint probability distributions via Archimedean copula functions. Total 30-year data of wave height, wind speed, and current velocity in the Bohai Sea are hindcast and sampled for case study. Four kinds of distributions, namely, Gumbel distribution, lognormal distribution, Weibull distribution, and Pearson Type III distribution, are candidate models for marginal distributions of wave height, wind speed, and current velocity. The Pearson Type III distribution is selected as the optimal model. Bivariate and trivariate probability distributions of these environmental conditions are established based on four bivariate and trivariate Archimedean copulas, namely, Clayton, Frank, Gumbel-Hougaard, and Ali-Mikhail-Haq copulas. These joint probability models can maximize marginal information and the dependence among the three variables. The design return values of these three variables can be obtained by three methods： univariate probability, conditional probability, and joint probability. The joint return periods of different load combinations are estimated by the proposed models. Platform responses （including base shear, overturning moment, and deck displacement） are further calculated. For the same return period, the design values of wave height, wind speed, and current velocity obtained by the conditional and joint probability models are much smaller than those by univariate probability. Considering the dependence among variables, the multivariate probability distributions provide close design parameters to actual sea state for ocean platform design.