A practical assessment of the impact of using multivariate statistical models in the design of coastal infrastructure

Abstract

Offshore and coastal infrastructure must be designed to withstand loading conditions that among others arise from extreme environmental conditions. Physical processes such as storm surges, tides, currents, and waves play an important role in the design of these structures. Several variables characterize the relevant physical processes (e.g. significant wave height, mean wave period, water level, wind speed) and a thorough analysis of these variables is required when dealing with offshore and coastal dynamics, durability, and reliability assessment.

Traditionally, a critical loading condition is defined by characteristic values of environmental variables that are determined based on the highest loads previously experienced. Modern design methods seek to derive loads that correspond to specified reliability by considering the frequency of a specific loading magnitude.Traditional design approaches do not take into account the interrelations and dependencies among the variables of interest. Hence, wrong representations of the physical processes and unnecessary conservative representations of the design loads might occur. This may severely limit their effectiveness and can lead to expensive and inappropriate decisions. Multivariate frequency analysis approaches currently receive much attention within the academic community, however, advanced statistical concepts such as regular vine copula are slow in being taken up by engineering practice.
This thesis presents a practical assessment and further development of a vine-based methodology, used for the derivation of design values, in continuation of the work performed by Sell´es Valls (2019). Regular vine copulae are advanced statistical models for high dimensional distributions using (conditional) bivariate copulae as building blocks. This study contributes to bridging the gap between the academic community and engineering practice on one hand, and on the other hand, contributes to a better understanding of the potential added value of incorporating dependence information in the design process of coastal and offshore infrastructure. It has a conceptual point of view where the concept of using dependence information by applying advanced statistical techniques is explored and the required adaptations throughout the entire design process are evaluated.
In this research, it is found that the multivariate vine-based methodology can be successfully incorporated in the design process of a breakwater structure, and on average results in minimal required dimensions of elements of the cross-sectional design that turn out to be smaller and the corresponding costs up to 25% lower compared to the univariate traditional approach. This is realized by adapting the framework enabling an offshore-nearshore transformation of the wave conditions using SWAN software. Furthermore, the theoretical framework is extended by introducing Kendall’s measure providing a suitable definition of the critical region from which the critical loading conditions can be obtained. It is concluded that the vine-based approach could act as a tool providing extra information about the behavior of the system and insights on the degree of conservatism of the traditional approach. The considered role of the vine-based methodology in the design process of a breakwater structure (or coastal infrastructure in general) is to provide the practitioner with additional insights supporting the traditional design approach and possibly optimizing the design.