Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines (TBMs). During the TBM tunnelling process, a large number of operation data are generated, reflecting the interaction between the TBM system and surrounding rock, and these data can be used to evaluate the rock mass quality. This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data. Based on the Songhua River water conveyance project, a total of 7538 TB M tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing. Then, through the tree-based feature selection method, 10 key TBM operation parameters are selected, and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers. The preprocessed data are randomly divided into the training set (90%) and test set (10%) using simple random sampling. Besides stacking ensemble classifier, seven individual classifiers are established as the comparison. These classifiers include support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), gradient boosting decision tree (GBDT), decision tree (DT), logistic regression (LR) and multi-layer perceptron (MLP), where the hyper-parameters of each classifier are optimised using the grid search method. The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers, and it shows a more powerful learning and generalisation ability for small and imbalanced samples. Additionally, a relative balance training set is obtained by the synthetic minority oversampling technique (SMOTE), and the influence of sample imbalance on the prediction performance is discussed.

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Keywords: Tunnel boring machine (TBM) operation data, Rock mass classification, Stacking ensemble learning, Sample imbalance, Synthetic minority oversampling technique (SMOTE)