## 6 Discussion
### Discussion of Research Limitations  
The classification recognition of surrounding rock based on RFECV-Optuna-XGBoost has a high accuracy. However, like all studies, it has limitations, such as a single data type, insufficient scale, and the fact that geological and rock mass parameters are not considered. If the established model is directly applied to other regions, it may not be able to obtain more optimized results. Therefore, the steps to establish the dataset are proposed, and the following three points are discussed.  
(1) Firstly, it is necessary to consider collecting more types of characteristic variable data through advanced geological prediction methods and to establish a dataset that comprehensively considers the rock-machine relationship. All of the data used in this study are numerical data, which will generate other data types, such as image type and text type, in the tunneling process. For example, in previous studies, the characteristics of rock slag were analyzed to verify the feasibility of rock slag in order to judge the surrounding rock conditions, and the identification and classification of the surrounding rock were realized based on the characteristics of the rock slag and the parameters. This means that when classifying other types of data, the data preprocessing process will be more complex and will require the analysis of multiple and multi-feature data types.  
(2) The identification of surrounding rock grades is usually a multiclass classification problem, and the main challenge in solving the identification problem is the phenomenon of class imbalance in the dataset, which will significantly impact the accuracy of the model. Therefore, when constructing the dataset, we thoroughly considered this factor and chose the method of sample size balance. Many approaches to overcoming the class imbalance problem have been proposed [57; 58]. The most commonly used methods involve the implementation of various class balancing algorithms, oversampling (such as SMOTE) [59; 60], undersampling, cost-sensitive learning [61; 62], or ensemble methods [63; 64; 65] tailored for imbalanced datasets; these are used to solve the problem of the uneven data scale distribution of different dominant lithologies in the dataset.  
(3) The lithologies involved in this study include tuff-breccia or tuff with tuffaceous breccia, sandstone, agglomerate, or volcanic breccia, tufaceous sandstone, granodiorite, and calcareous sandstone (Section 3.1). This system can provide a reference for similar types of TBM tunnel engineering. Further tests and optimization are needed for the different lithologies of engineering.  
In future work, we will focus on developing and optimizing the methods of identifying the surrounding rock grade, exploring the application of model generalization ability in different geological conditions, integrating the rock-machine relationship (including physical and mechanical properties such as rock strength, fracture development degree, and water content), and using field observation information to improve the accuracy and efficiency of the surrounding rock grade identification. Meanwhile, we will also develop a more intelligent real-time identification and early warning system for surrounding rock in TBM tunneling; this system will be able to monitor the real-time changes in the surrounding rock grade in TBM tunneling and adjust the tunneling parameters in time to adapt to different geological conditions. In addition, we also plan to focus on studying the variation law of TBM tunneling parameters before adverse geological disasters such as mud bursts, water inrush, large deformation, and rock bursts in order to provide adequate early warnings.