## 1 Introduction  
In recent years, the development and utilization of underground space have attracted increasing attention, involving many ultra-long tunnel projects in various major water diversion and transfer engineering schemes [1]. This has significantly promoted tunnel boring machine (TBM) application and development. Consequently, TBM construction has become the preferred method for super-long tunnel construction [2]. Super-long tunnel construction often faces challenges such as excessive length, deep buried depths, and complex geological conditions. Therefore, TBM's efficient excavation and construction safety have always been of great concern to related personnel. Compared with the traditional drilling and blasting method, TBM's continuous mechanized operation offers various advantages, including safety, speed, and high-quality outcomes. It is also environmentally friendly, reduces labor intensity, and enhances construction safety. The TBM construction speed can reach three to ten times that of the drill and blast method [3, 4]. However, there is often a significant difference between the actual and designed surrounding rock-grade conditions in practical construction. TBM tunneling shows poor adaptability when geological conditions change. Traditional methods of advanced geological forecasting often require additional tunneling time and equipment costs. In most cases, the relationship between the rock-machine interaction relies on the operator's experience and judgment, often failing to adjust the excavation parameters promptly according to changes in the surrounding rock grades of TBM tunneling. This leads to an inability of the TBM to utilize its efficiency advantages during tunneling. It would significantly reduce construction risks if it were possible to continuously monitor the changes in the surrounding rock conditions at the cutter head in real-time during TBM tunneling, thus selecting better excavation parameters. Tunnel surrounding rock classification is fundamental to understanding geological problems in tunnel engineering and remains a significant research area in underground construction. The common tunnel surrounding rock classification techniques are based on the stability of the surrounding rock. Traditional classification methods used domestically and internationally, such as the RQD [5], RMR system [6], Q system [7; 8], and BQ grading method [9; 10], are often suitable for traditional drilling and blasting construction. To meet engineering needs, numerous scholars have proposed various rock mass classification methods suitable for TBM construction. For instance, [PERSON] [11], based on the Q classification system and data from 145 tunnel constructions and geological information, proposed the QTBM model from the perspective of the rock-machine interaction. This model is used to predict the net tunneling rate and construction speed. Refining and modifying existing rock mass grades for TBM excitability classification has been widely applied in the TBM rock mass classification field [12; 13; 14; 15]. In addition, [PERSON] [16] proposed the RME scoring system based on the difficulty of rock mass excavation. Based on the proposed system, the comprehensive evaluation of whether the rock mass is easy to excavate, TBM selection cost estimation, and tunneling performance, predictions are completed during the actual construction process. [PERSON] [17] integrated TBM excavation and surrounding rock adaptability classifications. They took the TBM construction speed as the classification index to obtain a comprehensive classification method for surrounding rock in TBM construction. However, existing rock mass classification methods have shortcomings, such as having numerous influencing factors, complex grading systems, and a singular focus on excavation performance evaluation indicators. On the other hand, owing to the complex geological environment in TBM construction and the high cost of sampling and testing, it requires effort to obtain rock mass parameters accurately.