Intelligent recognition of surrounding rock grades based on TBM tunneling parameters

Abstract

Rapid, accurate, and efficient prediction of surrounding rock grades is crucial for ensuring the safety and enhancing the efficiency of tunnel boring machine (TBM) construction. To achieve intelligent perception of surrounding rock grades based on TBM tunneling parameters, this study leverages data from the TBM1 construction phase of the Luotian Reservoir-Tiegang Reservoir Water Diversion Tunnel Project, integrating geological records and tunneling parameters to establish models for different rock grades. First, raw data were cleaned and denoised using box plots, followed by the selection of eight critical parameters—including thrust, torque, penetration rate (PR), rotation speed (RS), et al—through a hybrid approach combining “knowledge-driven” and “data-driven” criteria. The dataset was partitioned into training, testing, and validation sets at a 7:2:1 ratio. Three data processing methods were applied, and machine learning algorithms (XGBoost, Random Forest, CatBoost, and LightGBM) were employed to construct surrounding rock classification models, with Optuna hyperparameter optimization implemented to enhance model performance. The result reveals that the CatBoost model, optimized via SMOTE (Synthetic Minority Oversampling Technique) and hyperparameter tuning, delivered superior performance, achieving 99% validation accuracy with no misclassification across adjacent surrounding rock grades. This research provides actionable insights for advancing intelligent TBM construction practices.