Fine-Scale Stratigraphic Identification Using Machine Learning Trained on Multi-Site CPTU Data

The piezocone penetration test (CPTU) provides rapid, continuous measurements of in situ geotechnical parameters, making it a valuable tool for soil classification and stratigraphic identification. However, conventional classification methods frequently exhibit poor cross-regional generalizability and remain limited in achieving fine-grained stratigraphic identification. To address these limitations, this study constructs a cross-regional CPTU soil classification dataset by integrating data from three sources: the Premstaller Geotechnik database, the Global-CPT/3/1196 database, and a Chinese engineering project database. The compiled dataset was subsequently partitioned into a training set of 454,184 samples and three independent test sets. Three feature combinations and four machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost), were evaluated in terms of classification performance and cross-regional robustness. Results indicate that the XGBoost-based model, using Depth, corrected cone resistance (qt), friction ratio (Rf), pore pressure ratio (Bq), normalized friction ratio (Fr), and pore pressure (u2) as inputs, achieved the highest performance across the three independent test sets. Misclassifications primarily occurred between adjacent soil types with similar physical characteristics. SHapley Additive exPlanations (SHAP) analysis indicated that Fr and qt were the dominant contributors to model predictions; Rf played an important role in minority classes; Depth showed relatively balanced importance across classes, while Bq and u2 made minimal contributions. Applying the best-performing model to unseen CPTU data and comparing the predictions with borehole logs showed that the model not only preserves overall stratigraphic trends but also identifies finer-scale stratigraphic details.