The design of offshore and coastal infrastructures, sand nourishment and other ’soft’ coastal interventions require the analysis of environmental variables (e.g. wind, waves, rainfall) that can potentially cause the failure of such structures. Processes such as overtopping, beach erosion, and coastal flooding can result from a combined action of two or more physical processes. Traditional infrastructure design practices assume the highest load previously experienced as the design load, regardless of possible interactions between variables (or processes). This may lead to a misrepresentation of critical design loads. This thesis presents a methodology for defining infrastructure design loads accounting for their interdependence. The methodology is general and is based on regular vines. Vines are graphical tools for defining high dimensional distribution functions through pair-copula construction. With this premise in mind, the main effort was concentrated in formulating a series of steps to integrate several stages of the design: from the processing of raw data up to the choice of design loads for any specific design purpose. The vine-based methodology was applied to the design of a breakwater in Galveston Bay, Texas. This application showed that accounting for the interdependence between design variables provides a more comprehensive description of the physical system acting on the infrastructure. However, the vine-based method is computationally demanding. Hence, the applicability of this methodology should be evaluated on a case by case basis. In parallel, the possibility to define goodness of fit test for vine-copula based on the concept of tree-equivalent classes is explored. The focus is on model selection strategies based on graphical and statistical properties of the vines. The main motivation to investigate model selection strategies for vines is the considerably large computational time needed to fit all regular vines in more than 6 nodes to the data. In this thesis, a novel algorithm is developed to facilitate the implementation of vines in higher dimensions (vines with more than 6 nodes). This algorithm significantly reduces the computational effort to select a regular vine by allowing the user to test only a subgroup of vines in n-nodes constructed on specific characteristics of the vines in (n-1)-nodes.