Comparative study on data-driven prediction of overconsolidation ratio using supervised machine learning models

Overconsolidation ratio (OCR) is an important geotechnical parameter that plays a crucial role in the analysis and design of foundations and structures on clay deposits. In this study, five machine learning (ML) algorithms, including gradient boosting machine (GBM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and eXtreme gradient boosting (XGB) were developed to estimate the OCR of clays based on the piezocone penetration test data. The ‘GridSearchCV’ function from the Scikit-learn package was employed to perform hyper-parameter tuning and k-fold cross-validation, ensuring the best possible model performance. Vertical total stress (σ_v0), hydrostatic pore water pressure (u₀), corrected cone resistance (q_t), pore pressure elements at the cone tip (u₁), and above the cone base (u₂), along with the type of clay (intact or fissured) were selected as the main features of input data. Sensitivity analysis revealed that q_t was the most influential parameter for RF, GBM, XGB, and SVM predictions, while all inputs affected the ANN model. It was found that the SVM model delivered the lowest accuracy in predicting OCR. In contrast, the XGB model showed the best performance, while the remaining models achieved reliable results, each with a coefficient of determination of 0.90 or higher.