## 4 Discussion  
1. The research results of this paper can be applied to actual construction and inspire practitioners. Due to its special characteristics, the cutterhead cuts the entire tunnel face during TBM tunneling, completely isolating the staff from the tunnel face. As a result, it is impossible to directly monitor the front surrounding rock during construction comprehensively; thus, quickly and accurately testing and evaluating the quality characteristics of the surrounding rock in the field are difficult. In this paper, we proposed a classification system for surrounding rock excavatability based on TBM rock-breaking parameters, and a method for the intelligent identification of this system was proposed. The system can be used to quickly and accurately identify surrounding rock excavatability in the field using real-time TBM excavation data. This allows the staff to quickly adjust TBM tunneling parameters and determine the wear of the rolling disc cutter. In addition, to avoid the limitations of the system proposed in this paper, i.e., the need to calculate fixed _SER_ and _PR_ indicators, we proposed using deep forest algorithms to achieve continuous and accurate intelligent identifications of surrounding rock excavatability. The research study in this paper is important for avoiding construction risks, controlling construction costs, improving TBM tunneling efficiency, and achieving TBM intelligent construction.
2. Generally, SRE varies continuously. Therefore, real-time TBM tunneling parameters can identify the SRE in a certain area behind the tunnel face and thus achieve the prediction of SRE. However, the actual excavation and geological conditions need to be combined with different projects to make reasonable judgments. It should be noted that the system and intelligent identification method established in this paper are applicable to the stable phase of TBM excavation cycles (Section 2.2.1).
3. The SRE classification system based on TOPSIS has consistent default index weights and should be reasonably analyzed for this problem in a subsequent study (Section 3.1.3). The lithologies involved in the data include tufaceous sandstone, sand conglomerate, granite, mudstone, quartz diorite, quartz schist, metamorphic and sandstone (Section 2.1). This system can provide a reference for TBM tunnel projects with similar lithologies. Further testing and optimization are needed for projects with different lithologies.
4. The intelligent identification model of SRE based on DF in this paper does not use multi-grained scanning in its structure (Section 3.2.2). However, whether multi-grained scanning is needed for input feature dimensions that are different from the model in this paper depends on the actual model situation.
5. The analysis of model performance reveals that surrounding rocks with good excavatability had lower identification accuracy than surrounding rocks with poor excavatability due to the overlapping data of surrounding rock tunneling parameters (Section 3.2.3). Further research is needed to eliminate or reduce the effect of data overlapping.
6. For projects with different geological conditions, the SRE process in Figure 13 can be referred to (Section 3.1). In the actual excavation, for rock mass with poor SRE (high hardness and intactness), excavation parameters should be adjusted in a timely manner, and worn cutters should be replaced to improve the utilization rate of TBM tunneling. In addition, for rock mass with high intactness, hardness, and brittleness, rockburst should be a focus of attention. For rock mass with good SRE (soft and fracturing rock), timely support should be provided to improve excavation efficiency.