## 2 Research Frameworks and Methodologies  
### Research Frameworks  
The research framework for the surrounding rock grade recognition of the TBM tunneling method proposed in this study is shown in Figure 1. The framework consists of four main parts: (1) data acquisition and processing; (2) feature selection based on RFECV; (3) the establishment of the identification model based on Optuna-XGBoost, with practical engineering validation and performance evaluation; and (4) the measurement of the model features.  
(1) By collecting data on the TBM excavation parameters under different surrounding rock grades in the KS Tunnel), an initial dataset for the prediction model was formed after selection and integration. In this dataset, the TBM tunneling parameters were utilized as input variables, and the surrounding rock grade was employed as the output variable. It should be noted that, due to the minimal occurrence of Grade V surrounding rocks (1.6% total) in this project, only Grades II to IV were considered during the identification process. The dataset was divided into a training set and a test set in a 7:3 ratio. The training set was used for model learning, and the test set was used to verify the model's accuracy.  
(2) To ensure data quality, preprocessing operations such as cleaning, outlier handling, and missing value treatment were conducted on the processed data. Using box plots from mathematical statistics, eight input features were identified as the initial feature set. These features included the thrust (F), rotation speed (N), torque (T), net penetration rate (PR), penetration depth (p), field penetration index (FPI), torque penetration index (TPI), and specific excavation energy (SE). To address the common issues of high complexity and redundancy in the production data, we employed a method combining RFE with CV, using RF as the base model to complete the feature selection; thus, we avoided manual intervention automatically. This method utilized RF and CV to obtain the training score for the current dataset and calculated the importance of each feature. We then eliminated the least essential feature and repeated the training and elimination steps until the dataset was empty. Finally, the dataset with the highest training score was selected as the feature subset after feature selection. This feature selection method ensures data quality while reducing redundant features and shortening the model training time.  
(3) The establishment of an Optuna-XGBoost identification model with practical engineering validation and comparison. By automatically optimizing hyperparameters using Optuna, the Optuna-XGBoost model was constructed and applied to the KS Tunnel  
Figure 1: Research framework: (**a**) overview of the research section and (**b**) four main parts of framework.  
to identify the surrounding rock grades of the TBM construction section in the tunnel. The effectiveness of this model was verified based on evaluation metrics, and its superiority was highlighted through performance comparisons with other models.  
(4) Model feature measurement. During the training process of the XGBoost model, a global importance measurement of the features was carried out using metrics such as accuracy, F1 score, and recall rate. In addition, comparative experiments were conducted with random forest (RF) and decision tree (DT) models that were optimized through Optuna to highlight the superiority of the model presented in this study.