## 1 Introduction  
With the rapid construction of railway, hydraulic, subway, and subsea tunnels, tunnel boring machines (TBMs) play an important role in tunnel construction because of their advantages over traditional drilling and blasting methods: high safety, high efficiency, and environmentally friendly nature [1, 2]. However, TBM construction requires more demanding geological conditions. Under appropriate surrounding rock excavatability (SRE) conditions, TBMs can achieve high rock-breaking efficiency. Once encountering hard surrounding rock with good integrity, TBM shows serious rolling disc cutter wear and low tunneling efficiency [3; 4]. This can easily increase budgets and cause construction delays [5]. In addition, the excavation can also cause significant disturbance to the surrounding rock's stability [6; 7]. Therefore, proposing an SRE classification system for TBM tunneling and realizing the accurate and intelligent identification of excavatability classification are significant in order to adjust tunneling parameters and control construction costs. TBM tunneling processes can be characterized by surrounding rock-machine interactions. Thus, TBM tunneling parameters, e.g., penetration and cutterhead torque, significantly interact with the surrounding rock, and many effective studies are available [8]. For example, [PERSON] et al. [9] established the relationship between TBM tunneling and geological parameters and found that surrounding rock strength was proportional to the field penetration index (FPI). [PERSON] et al. [10] reported that FPI had a linear proportional relationship with rock strength. [PERSON] et al. [11] used TBM tunneling parameters to predict the geological parameters of surrounding rock based on neural networks and simulated annealing algorithms. [PERSON] et al. [12] used rock parameters to predict the penetration rate of TBMs based on artificial neural networks. These studies indicate that TBM tunneling parameters and the geological information of surrounding rock can invert each other. Thus, surrounding rock properties can be analyzed using the parameters based on the rock-breaking performance of TBMs. Considering the significant interaction between TBM tunneling parameters and rock mass, various effective methods have been proposed to establish relationships between TBM tunneling parameters and geological parameters. Traditional surrounding rock classification methods, such as rock mass rating (RMR), tunneling quality index (Q-system), and basic quality index (\(BQ\)) systems, have difficulty guiding TBM construction effectively [13]. Therefore, some methods have been proposed for surrounding rock classification and performance prediction for TBM tunneling, such as the Colorado School of Mines (CSM) model, the Norwegian University of Science and Technology (NTNU) model, the \(Q_{TBM}\) model, the rock mass excavatability (RME) rating and classification system, and the robust classification method (RCM) [14; 15; 16; 17; 18]. All these methods have addressed the problem that traditional surrounding rock classification methods cannot cope with TBM construction to a certain extent, with better performances. In terms of SRE, some different models have also been proposed. For example, [PERSON] et al. [19], [PERSON] et al. [20], and [PERSON] et al. [21] used FPI as the evaluation index and considered various geological parameters, such as total hardness, uniaxial compressive strength of rock (\(R_{c}\)), rock quality designation (RQD), structural surface conditions, structural surface spacing, and the angle between the tunnel and structural surface, to some extent in order to build their models. In addition, specific penetration and penetration were also used as evaluation indexes for SRE evaluation and prediction [22; 23; 24]. It should be noted that the existing models involve many factors and have complicated application processes. SRE is mainly evaluated using a single index.