Data-driven Probabilistic Seismic Demand Modeling of Offshore Wind Turbines

The seismic assessment of monopile-supported offshore wind turbines (OWTs) under multi-hazard loading is challenging, as traditional simulations are computationally prohibitive for large-scale probabilistic studies. This study introduces a data-driven framework to develop efficient surrogate models for predicting seismic responses and Probabilistic Seismic Demand Models (PSDM). A comparative analysis of 16 machine learning and deep learning algorithms reveals that advanced deep learning models, particularly the Kolmogorov-Arnold Network (KAN) and Multi-Layer Perceptron (MLP), significantly outperform traditional techniques. The KAN model achieved the highest accuracy, producing a robust surrogate capable of nearinstant predictions. This drastically reduces the computational cost of seismic risk assessments, making large-scale probabilistic analyses feasible.