Ensemble Machine Learning for Predicting TBM Penetration Rate with Limited Geotechnical Data

Accurate prediction of TBM penetration rate (ROP) is of critical importance for the planning of tunneling operations and performance assessment. In this study, both classical multiple linear regression (MLR) and machine learning approaches—namely Random Forest, Bagged Trees, Support Vector Machine, and LSBoost—were employed to investigate the contributions of BI, UCS, DPW, α, and BTS parameters to ROP prediction. Univariate and MLR analyses exhibited limited explanatory power (R2 = 0.365), confirming that ROP is governed by complex, multivariate, and nonlinear interactions. Comparative machine learning analyses revealed that LSBoost provides the most reliable predictions, achieving the highest accuracy (R2 = 0.9565) and the lowest error metrics (RMSE = 0.1794; MAPE = 5.63%) for both original and normalized datasets. While Random Forest and Bagged Trees demonstrated comparable performance, SVM showed limited predictive capability on the original dataset (R2 = 0.452; RMSE = 0.637; MAPE = 18.60). However, its performance improved substantially following data normalization, approaching that of LSBoost (R2 = 0.936; RMSE = 0.218; MAPE = 4.87). Feature importance analyses based on PDP-driven Jacobian sensitivity and SHAP methods indicate that UCS, BI, and DPW are the dominant factors governing TBM penetration performance, while also demonstrating that model outputs remain interpretable in an interaction-aware manner. These findings highlight that machine learning-based approaches can deliver both reliable prediction and interpretability even with small and heterogeneous datasets, and suggest that future research should focus on integrating larger datasets, hybrid modeling strategies, and advanced explainability techniques.