Abstract
Abstract Geological characteristics (GC) play a pivotal role in the efficiency and safety of tunnelling operations, necessitating accurate prediction methods. This study utilizes a dataset comprising operational parameters such as mean thrust (MT), penetration rate (PR), field penetration index (FPI), torque penetration index (TPI), advance rate (AR), mean cutterhead torque (MCT), and specific energy (SE). Bayesian optimization (BO) is employed for hyperparameter tuning of the extra trees (ET) algorithm to enhance prediction accuracy. The model was tasked with predicting three GCs using the operational parameters. The ET-BO model achieves near-perfect performance across all three GC classes, with an F1 score and accuracy of over 95%, during training. In testing, the model maintains strong performance with an F1 score and accuracy of over 91%, demonstrating robust predictive capability across all classes.