Cumulative strain intelligent evaluation of marine soil from offshore wind farms based on enhanced machine learning

Abstract

Accurate evaluation of cumulative strains in marine soils under long-term cyclic loading is essential for the design and safe operation of offshore wind turbines. This study proposes an enhanced machine learning model to predict the cumulative strain in marine soils subjected to cyclic loading. Cumulative strains of marine soils from five offshore wind farms under long-term cyclic loading were tested. Four prediction models for cumulative strains were developed and evaluated based on test results using the Back Propagation Neural Network (BP-NN), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) models, each combined with the Particle Swarm Optimization (PSO) algorithm. The prediction model with the highest accuracy was further analyzed using the SHapley Additive exPlanations (SHAP) method. Results show that the RF and XGBoost algorithms have higher prediction accuracy, with R² values above 0.99, compared to the BP-NN and SVR models. Furthermore, dynamic triaxial test parameters significantly influence the cumulative strain predictions more than the soil properties. This study provides a more efficient method for cumulative strain assessment of marine soils under long-term cyclic loading.