Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Due to the complexity of underground engineering geology, the tunnel boring machine (TBM) usually shows poor adaptability to the surrounding rock mass, leading to machine jamming and geological hazards. For the TBM project of Lanzhou Water Source Construction, this study proposed a neural network called PCA–GRU, which combines principal component analysis (PCA) with gated recurrent unit (GRU) to improve the accuracy of predicting rock mass classification in TBM tunneling. The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA–GRU model. Subsequently, in order to speed up the response time of surrounding rock mass classification predictions, the PCA–GRU model was optimized. Finally, the prediction results obtained by the PCA–GRU model were compared with those of four other models and further examined using random sampling analysis. As indicated by the results, the PCA–GRU model can predict the rock mass classification in TBM tunneling rapidly, requiring about 20 s to run. It performs better than the previous four models in predicting the rock mass classification, with accuracy A, macro precision MP, and macro recall MR being 0.9667, 0.963, and 0.9763, respectively. In Class II, III, and IV rock mass prediction, the PCA–GRU model demonstrates better precision P and recall R owing to the dimension reduction technique. The random sampling analysis indicates that the PCA–GRU model shows stronger generalization, making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage. Highlights A neural network combining principal component analysis (PCA) and gated recurrent unit (GRU) is proposed to provide accurate prediction of rock mass classification in tunnel boring machine (TBM) tunneling. The PCA–GRU model runs in approximately 20 s, which enables quick prediction of rock mass classification in TBM tunneling. The PCA–GRU model shows stronger generalization, making it more suitable in conditions where the distribution of various rock mass classes and lithologies change in percentage. This study considers rock mass as three different classifications: Class II, III, and IV. The initial data set collection included nine parameters: uniaxial compressive strength, Brazilian tensile strength, rock integrity index (Kv), rock wear resistance index (CAI), deformation modulus (E0), Poisson's ratio (μ), net tunneling rate (PR), rot