Design loads and reliability assessment of marine structures considering statistical models of metocean data

The objective of this study is to investigate the effect of statistical models for constructing the bivariate distribution of metocean data on design loads and reliability assessment of offshore structures. First, the conventional conditional joint distribution and single copula models are utilized for characterizing the statistical data. Additionally, a novel mixed copula model is developed using a linear combination of Gaussian, Clayton, Gumbel and Frank copula considering various tails characteristics, the parameter estimation is performed using expectation-maximization (EM) algorithm. Moreover, a mixed joint model based on linear combinations of simple statistical models is proposed. Then, the environmental contours are constructed based on various joint probability distribution of environmental variables. Finally, the reliability of marine structures is estimated using direct Monte Carlo simulation considering dependence structure and joint models of statistical data. The results indicate the reliability of offshore structure differs significantly because the bivariate distribution models of environmental variables could not be defined uniquely, the dependence structure has non-negligible effect on reliability assessment. A mixed copula can be suitable for describing variables with symmetrical or asymmetric, independent or correlative, upper or lower tails, or complex characteristics. The mixed joint model provides satisfactory results for marginal and joint fitting.