# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
[24] predicted the performance parameters of a TBM using a deep belief network (DBN) in the excavation of a water diversion project tunnel in northeast China. [PERSON] et al. [25] proposed a BP neural network (SA-BPNN) model combined with a simulated annealing algorithm for predicting rock mass parameters, including uniaxial compressive strength (UCS), in TBM tunnels. [PERSON] et al. [4] developed a deep neural network model based on measured data from a water diversion project, which realised the performance parameters of TBM excavation processes to identify the surrounding rock in real time. [PERSON] et al. [26] investigated a deep learning-based method for real-time identification of rock fragments during TBM excavation, which used an instance segmentation model to identify the rock fragments. [PERSON] et al. [27] developed a model based on a Long Short-Term Memory (LSTM) network for analysing and predicting the tunnel surrounding rock deformation. The majority of these studies utilise deep learning algorithms to perceive the intrinsic patterns of TBM data, thereby transforming the problem of predicting the enclosing rock grades into the problem of classifying the enclosing rocks or predicting the rock mass parameters. This approach is more automated and universal. However, it should be noted that the majority of the data utilised in these studies originated from a single data source. Moreover, there was a paucity of consideration given to the joint influence of multi-source data (e.g., HSP data, TBM data, 3D spatial data, etc.) on rock body state perception. Moreover, the majority of these studies overlooked the consideration of the causal relationship between the data. The incorporation of this factor would facilitate the enhancement of the ability to discern the intrinsic patterns in the data, as well as the interpretation of these patterns, thereby optimising the prediction effect [28]. The following key technical issues are discussed in this study: (1) How to fully extract the feature information of multi-source data and use it for surrounding rock grade prediction. (2) How to consider the causal relationship between multi-source data to improve the prediction accuracy of TBM surrounding rock grade. (3) How to verify the accuracy and robustness of the proposed TBM surrounding rock grade prediction model and improve the interpretability of the model. The main contributions of this study are as follows: (1) Based on the horizontal acoustic profile method (HSP), the reflected wave signal is detected and analysed as additional feature information, and the calculation of various rock fragmentation indexes is introduced. (2) A hybrid neural network model (TRNet) based on multi-source feature fusion is proposed, which combines causal discovery to achieve interpretable TBM surrounding rock grade prediction while maintaining high accuracy. (3) The superiority of the model structure is proved by ablation experiments and comparative experiments, and the contribution of each input feature is explained by SHapley Additive exPlanations (SHAP). This represents a genuine instance of the integration of tunnel engineering with deep learning technology. Numerical simulation and intelligent algorithms are utilised in conjunction with the characteristics of multi-source data to achieve real-time prediction of surrounding rock grade in complex and changeable rock mass conditions during the process of tunnel excavation. This method is expected to provide auxiliary decision-making for engineering rock mass technology and rock disaster prediction technology in the field of rock mechanics. The overall flowchart of this study is shown in Figure 1.  
The remaining sections are organised as follows: Section 2 presents the construction process of the novel hybrid neural network (TRNet) proposed in this study and the various algorithms it relies on, and describes how the model is evaluated and interpreted. Section 3 presents the data preprocessing methods and data analysis results applied to a tunnel project in western Sichuan, China.