Genetic-Algorithm-Based Approach for Wind Turbine Foundation Optimisation

Abstract

Designers traditionally perform the preliminary sizing of foundations based on engineering judgement, relying on parameters such as superstructure loads and the characteristics of the supporting soil. This process must adhere to strict guidelines for wind turbine foundations to ensure structural stability and compliance with regulatory standards. This study proposes a computational model based on genetic algorithms to optimise the dimensions of wind turbine foundations. The fitness function combines two normalised objectives, namely concrete volume and bending moment, while structural and geotechnical requirements are imposed as constraints. The model was validated using six real-world case studies, achieving consistent reductions in concrete volume compared with the original designs, with an average reduction of 19.5%. Each case was run 10 times to assess the consistency of the solutions obtained. The results demonstrate the effectiveness of the proposed approach in identifying more material-efficient foundation geometries while satisfying the adopted design constraints. It should be emphasised that the reported savings refer specifically to concrete volume reduction and should not be interpreted as total foundation cost savings, since reinforcement design and detailing are outside the scope of the present model. This study is restricted to foundations with circular cross-sections, thereby opening avenues for future research aimed at extending the optimisation framework to alternative geometric configurations and incorporating reinforcement design.