Geotechnical engineering faces challenges in subsurface characterization due to reliance on sparse site investigations and empirical correlations, which limit the accuracy of soil property estimations. This study addresses this issue by leveraging machine learning (ML) methods to predict cone penetration test (CPT) measurements using data from driven cast in-situ (DCIS) pile installations. A comprehensive dataset, including cone resistance, sleeve friction, and friction ratio measurements, was used to train and evaluate ML models—Random Forest (RF), eXtreme Gradient Boosting (XGB), and TabNet. Among these, XGB trained at a 30 cm depth resolution demonstrated a strong balance of predictive accuracy, efficiency, and granularity, achieving R2 values exceeding 80% for cone resistance predictions even with limited data availability. By improving predictions at unseen locations, the study showcases the feasibility of integrating ML models into geotechnical workflows to enhance real-time decision-making, optimize construction practices, and support the development of digital twins for pile-supported structures.
Interpretability methods, specifically SHAP values, provided insights into feature significance and highlighted the critical roles of depth, spatial coordinates, machine-related features and representative sampling strategies in achieving reliable predictions. These findings demonstrate ML’s potential to align predictive capabilities with engineering principles, bolstering confidence in its adoption for practical applications.