The offshore wind energy market is expanding and the number of offshore wind turbines being installed in the near future is rising. Offshore wind turbines are being installed further offshore and in deeper waters. Besides, to lower the cost of wind energy offshore wind turbines are increasing in size and power output. Both wind turbines and their support structures are expected to keep increasing in size and weight in the coming years. After fabrication, wind turbine components and support structures have to be transported to onshore storage depots or to their offshore location. To enable safe transports, wind turbine components and support structures are constraint to heavy transport vessels or transport barges by sea fastening structures. As a result of wind turbine components and support structures increasing in size and weight, the reaction loads for which sea fastening structures need to be designed are increasing as well. Since increasing reaction loads have various negative consequences which are expected to become more critical for future transports, there is a need for an optimized reaction load calculation.

The current method of calculating the reaction loads which is widely used in the industry is often referred to as being a conservative method. The aim of this thesis was to enhance the existing calculation method of the reaction loads by shifting from a conservative approach towards a method of calculating reaction loads based on an acceptable probability of occurrence during transports. By calculating the reaction loads for an acceptable probability of occurrence it is avoided that sea fastening structures are designed for overly conservative reaction loads while the structural reliability of these structures will still be ensured.

In this thesis an existing sea fastening design project from the industry was used to perform a case study. Data and information from this project were used as input to perform motion analyses of a vessel which is transporting a jacket support structure. The obtained linear wave-induced accelerations of the jacket CoG were used as the main input for calculating the reaction loads. It was first investigated how these 6-DoF accelerations of the CoG are used in the current method of calculating the reaction loads. This was followed by introducing statistical extreme value theory with the purpose of using the accelerations of the jacket CoG as input for a probabilistic method of calculating the reaction loads.

The findings of this research show that optimized reaction loads can be obtained by replacing the current calculation method by a long-term probabilistic method. It was found that this long-term probabilistic method could be derived by combining 3-hour extreme value density functions of reaction loads with the probabilities of encountering the various sea states at the location on the route for which the most severe environmental conditions are expected. The long-term probabilistic method was used to perform a probabilistic investigation of the reaction loads calculated with the current calculation method. It was found that the return periods of the reaction loads calculated with the current method were significantly different for the individual jacket legs. Moreover, it was found that the sea fastening design for at least one of the jacket legs was expected to be over-conservative. By presenting the long-term probabilistic calculation method, a methodology was introduced which determines reaction loads based on acceptable return periods while avoiding over-conservative sea fastening designs.

This research has provided a new insight into the method of designing sea fastening structures. The long-term probabilistic calculation method can be applied in practice to determine optimized reaction loads incorporated in sea fastening designs. This research therefore makes a valuable contribution to preparing the reaction load calculation method for future transports which are expected to become more critical due to wind turbine components and their support structures growing in size and weight.