Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining

Currently, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge due to the complex interactions between the TBM and rock mass. In this study, the research work is based on part of a metro tunnel project that covers 2,083.94 m. The Gaussian mixture model (GMM) and K-nearest neighbor algorithm (KNN) are used to classify and predict the rock mass drillability in the TBM excavation process. Drillability indexes are introduced to cluster and classify the rock mass, including the penetration (P), field penetration index (FPI), torque penetration index (TPI), and specific energy (SE). Statistical characteristics of the drillability indexes were analyzed, and it was found that their distributions did not conform to the normal distribution, with large variation coefficients. Clustering analysis was then conducted on the TPI and FPI within the training group using the Gaussian mixture model , and six drillability categories of rock mass were classified. Subsequently, the mapping relationship between the cutterhead speed, advance speed, total advance force, and cutterhead torque in the training group and the drillability of rock mass was established based on the KNN classification model. It was revealed that when the K-value is set to 4, the model has high macro - F 1 , macro - P , and macro - R . Validated by the testing group data, this method has been proven to be feasible and effective. The research results indicate that this method can effectively classify and predict the drillability of tunneling surrounding rock mass in shield construction, particularly when the rock mass at the shield face is uniform and homogeneous. This provides a theoretical basis and technical support for safe and efficient shield tunneling.