## 1 Introduction
Consequently, this hinders the ability to perform real-time and accurate judgments of the surrounding rock grades based on rock mass parameters. A substantial amount of research has confirmed that there is a significant correlation between rock mass parameters and TBM excavation performance. [PERSON] [18] proposed the CSM model, which uses UCS and BTS to predict the net excavation rate of TBMs. [PERSON] [19] proposed the NTNU model, which achieves TBM performance prediction through the regression analysis of various rock mass parameters and TBM excavation parameters. [PERSON] [20] established a relationship model between surrounding rock parameters (UCS, DPW, and \(\alpha\)) and FPI based on PSO-SVR and, based on the correspondence between the FPI value and the excavatability of the surrounding rock, established a grading method for the surrounding rock applicable to TBM construction. Many empirical models and artificial intelligence methods demonstrate a strong correlation between surrounding rock and TBM tunneling parameters. These methods suggest that TBM tunneling parameters can be used in inverse analysis to achieve real-time perception and the identification of surrounding rock information during tunneling. For example, [PERSON] [21] established a new surrounding rock classification system by clustering TBM tunneling parameters and then effectively identifying the TBM tunnel surrounding rock grade based on SVM. Using the AdaBoost algorithm, [PERSON] [22] proposed a method for predicting the HC classification of surrounding rock based on the TBM excavation parameters. [PERSON] [23] used FPI and TPI as input parameters and improved the surrounding rock grade identifying model through the SOM-SVM algorithm. [PERSON] [24],analyzed TBM tunneling parameters and loaded distribution patterns under different surrounding rock conditions, using the RF algorithm with inputs such as N, T, PR, and F and with the surrounding rock grade as the output to establish a surrounding rock grade recognition model. [PERSON] [25] first analyzed the correlation between rock mass parameters and various TBM excitability assessment indices. Then a capability classification of surrounding rock based on TBM tunneling performance using the TOPSIS method effectively perceived and identified the proposed rock mass excitability grades used for four TBM tunneling parameters: F, PR, N, and p.  
The above studies have indicated that machine learning techniques exhibit promising applications in the recognition of TBM surrounding rock grade recognition. However, there are still some areas for improvement in the current research. Most studies primarily focus on the comparison of different machine learning algorithms, while overlooking the influence of variations in hyperparameters on the prediction and recognition outcomes of these algorithms. Although optimization algorithms have been employed in a few studies to identify the optimal parameter combinations for specific machine learning algorithms, thereby enhancing model optimization, it is important to note that the applicability of a fixed model in rock mass information perception cannot be guaranteed. Therefore, the optimal algorithm and its corresponding parameter combination is crucial for the accurate identification of the TBM surrounding rock grades. In response to these challenges, the study is based on a real-time dataset of TBM tunneling parameters obtained from a water supply tunnel in Xinjiang (referred to as the KS Tunnel). For this study, we proposed a novel model for identifying surrounding rock grades based on RFECV-Optuna-XGBoost. The method combined recursive feature elimination (RFE) with cross-validation (CV) to select a superior feature subset. It uses a random forest (RF) as the base model to evaluate feature importance and further reduce dimensionality to obtain the optimal feature subset, thereby enhancing the accuracy of the TBM surrounding rock grade recognition model.