Abstract: Structural optimization plays a crucial role in reducing the cost of offshore wind power, particularly in deep-water regions where the weight of jacket foundations increases substantially. However, there is ongoing debate regarding the water-depth range that is suitable for jacket foundations, and the threshold where floating foundations become more viable. Existing studies have not quantitatively analyzed how water depth affects jacket foundation mass, and have often struggled to handle the high dimensionality and stringent constraints inherent in jacket foundation optimization problems. In this study, we propose an optimization framework that couples parametric finite element analysis with a genetic algorithm to minimize the mass of jacket foundations based on three actual engineering projects at varying water depths. A novel population initialization strategy incorporating engineering experience-based solutions is introduced to improve convergence efficiency and solution quality. Comparative analysis against preliminary designs and existing offshore wind projects demonstrates the model’s ability to achieve cost-effective solutions, specifically reducing required jacket masses by 18.66%, 20.98%, and 17.22% at depths of 30.06, 60.23, and 89.81 m, respectively. The results reveal a 122.94% increase in jacket mass—from 1431.28 to 3190.90 t—as water depth increases from 30.06 to 89.81 m. The jacket foundation demonstrates superior cost effectiveness in shallow to moderate water depths, as the unit weight per megawatt (MW) of floating foundations is 97.51% and 35.74% higher at water depths of 60.23 and 89.81 m, respectively. Accordingly, the applicable water-depth threshold between the jacket and floating foundations is estimated to be approximately 100 m. The proposed optimization model offers a novel methodology and practical insights for the optimal design of offshore wind turbine support structures in varying marine environments.

Key words: Structural optimization; Jacket foundation; Genetic algorithm; Offshore wind power; Population initialization; Parametric modeling

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