An artificial intelligence-powered design strategy for offshore wind turbine monopiles

Monopiles support 60 % of existing offshore wind turbines (OWTs). Their effective design and modeling remains expensive and challenging due to complex nonlinear soil-structure interaction (SSI) under varying loads. This study aims to address these issues via an artificial intelligence (AI) model, named pAIle as the combination of “pile” and “AI”, using the long short-term memory (LSTM) model, trained on over 100 experimentally validated, high-fidelity finite element simulations. A new data structuring method has been introduced for LSTM training, where long-sequence data is temporally stacked via feature enrichment. This approach speeds up training by 10-fold while enhancing prediction accuracy, improving the overall R² from 0.983 to 0.995. Testing results showed that pAIle efficiently predicts pile head displacements and rotations both at small strains and in the post-failure flow state by reproducing nonlinear SSI, such as damping and cyclic accumulation of plastic strains. Feature importance analysis showed that pAIle correctly understands which physical parameters govern pile head deformations. Exceptional extrapolation performance, evident in an order of magnitude lower normalized mean squared error compared to similar AI models, underscores pAIle's generalization capacity. Comparative studies with other popular AI architectures further demonstrated the effectiveness of LSTM model. Finally, the paper illustrates how this approach can be integrated into existing engineering workflows by enabling rapid monopile size optimization and post-storm integrity assessments. The procedures can complete in less than 2 s on a personal computer, requiring only readily available soil or pile parameters, showing that it is a feasible novel design strategy for OWT monopiles.