Analysis of Offshore Pile–Soil Interaction Using Artificial Neural Network

Offshore wind power is one of the primary forms of utilizing marine green energy in China. Currently, near-shore wind power predominantly employs monopile foundations, with designs typically being overly conservative, resulting in high construction costs. Precise characterization of the interaction mechanisms between marine piles and surrounding soils is crucial for foundation design optimization. Traditional p-y curve methods, with simplified fitting functions, inadequately capture the complex pile–soil behaviors, limiting predictive accuracy and model uncertainty quantification. To address these challenges, this research collected 1852 empirical datasets of offshore wind monopile foundation pile–soil interactions, developing p-y curve and horizontal displacement prediction models using artificial neural network (ANN) expressions and comprehensive uncertainty statistical analysis. The constructed ANN model demonstrates a simple structure with satisfactory predictive performance, achieving average error margins below 6% and low to moderate prediction accuracy dispersion (26%~45%). In contrast, traditional p-y curve models show 30%~50% average biases with substantial accuracy dispersion near 80%, while conventional finite element methods exhibit approximately 40% error and dispersion. By strictly characterizing the probability cumulative function of the neural network model factors, a foundation is provided for reliability-based design. Through comprehensive case verification, it is demonstrated that the ANN-based model has significant advantages in terms of computational accuracy and efficiency in the design of offshore wind power foundations.