Estimation of TBM Penetration rate using Gradient Boosting-based Algorithms

Abstract TBM performance prediction from the rate of penetration (ROP) point of view has yet to draw a lot of attention since it is one of the main challenges for mechanized excavation with tunnel boring machines (TBMs). In this study, five algorithms, Gradient Boosting (GB), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), AdaBoost (AB), and CatBoost (CB) have been conducted to predict the ROP based on the Gradient Boosting theory. Six tunnel cases from different projects were examined to obtain the aim of the research. Dataset developed using those tunnel datasets includes Uniaxial compressive strength (UCS), Rock Type, Distance between Plane of Weakness (DPW), and TBM-related parameter of thrust force (TF). Mentioned Gradient Boosting algorithms were performed to obtain the most accurate results for the study. The developed models showed that XGBoost outperformed the other models, followed by the CatBoost model according to seven different evaluation metrics used to rank the models. After parameter tuning, the GB model outperformed others while those were not improved very much. By using the overall ranking according to the metrics and considering the parameter tuning time, XGBoost and CatBoost presented the first two best performances. Through SHAP values and dependency plots, the features and importance of the inputs showed the TF has the highest impact on the ROP, followed by UCS, Rock Type, and DPW. It is concluded that the XGBoost and CatBoost algorithms could be used for modeling to obtain the TBM penetration for similar rock types.