Dynamic Prediction of Rock Mass Classification in the Tunnel Construction Process based on Random Forest Algorithm and TBM in situ Operation Parameters

Abstract

Accurately predicting the rock mass classification is of great significance to ensure the safe and efficient construction of tunnel boring machines (TBMs). On the basis of the TBM in situ operation data recorded during tunnel construction, a prediction model for rock mass classification using the random forest (RF) algorithm is proposed. Through data preprocessing, 7538 TBM excavation cycles were obtained to form a data set. Each data sample included 195 operation parameters and corresponding information on mileage and rock mass classification. Furthermore, 6784 samples were randomly selected as the training set and the remaining 754 samples as the test set. According to the changing characteristics of operation parameters, each TBM excavation cycle was divided into the empty-push phase, the rising phase, and the stable phase. On the basis of the mean decrease Gini index, seven machine parameters highly correlated with rock mass classification were selected. Then, the variation characteristics (i.e., mean value and linear fitting slope) of the seven operation parameters in the first 30 s of the rising phase were used as the input features of the RF model. Additionally, the hyperparameters in the RF model were analyzed. The quantitative results show that the prediction accuracy is up to 87.27%, indicating that the proposed model is effective for the prediction of rock mass classification.