Genetic Algorithm-Based Design Optimization of Jacket Pile Foundations for Offshore Wind Turbines
This study addresses the design redundancy inherent in four-pile jacket foundations for offshore wind turbines by developing a Python-based parametric modeling and computational framework integrated with the Structural Analysis Computer System (SACS). Coupled with a genetic algorithm (GA), the framework establishes an intelligent optimization model for jacket pile foundations. The model is validated against an initial design and benchmarked against similar offshore wind projects. Results demonstrate rapid convergence: 93.46% of the final optimized solution is achieved within the first 20 generations. The optimized design reduces pile foundation mass by 22.35% (236.40 t) relative to the initial design, yielding cost savings exceeding one million RMB per turbine. The optimization strategy achieves material efficiency by shortening pile length, reducing wall thickness, and increasing pile diameter, while maintaining bearing capacity and controlling deformation. These outcomes confirm the effectiveness of the GA-based approach in balancing structural safety and economic performance, providing a robust design workflow for deep-water offshore wind applications.