# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 5 Conclusions  
In this paper, a novel hybrid neural network model, TRNet, based on multi-source feature fusion, is proposed. This model is based on real engineering data from a tunnel face in western Sichuan, China, with the aim of instantly predicting the surrounding rock grades in front of the current tunnel face of a TBM. The model incorporates the joint influence of multimodal data on the rock state and the causal relationship between multi-source data, and consists of multiple network modules, which can achieve efficient and accurate surrounding rock grades prediction. Concurrently, the model and other prevalent algorithms are evaluated by a series of statistical metrics, including accuracy, AUC (area under curve) and the Kappa coefficient. Subsequently, the model was analysed for interpretability using SHAP. The following conclusions were drawn:  
(1) In the test set, the mean accuracy, AUC and Kappa coefficient of TRNet are 92.15%, 95.90% and 74.56%, respectively. These three evaluation metrics are on average 2.26% higher than those of CNN, 2.66% higher than those of LSTM, 2.50% higher than those of KAN, 16.34% higher than those of KNN and 7.85% higher than those of XGBoost. Conversely, the TRNet model demonstrates the lowest number of misclassifications, thereby substantiating its efficacy in predicting surrounding rock grades. (2) The findings of the ablation experiments demonstrate that the multi-network module fusion strategy adopted by TRNet is an effective method to enhance the prediction ability of the model. The relative importance of various network modules in the prediction ability of TRNet is as follows: Direct-LiNGAM > CNN > KAN. (3) The results of the interpretability analysis of TRNet using the SHAP method show that the HSP data contributes the most to the surrounding rock grade prediction, followed by 3D spatial data, and lastly rock fragmentation indicators (TPI, FPI) and TBM data. Among several common boring parameters, the variation of cutter torque (\(T\)) tends to be more important for surrounding rock grades prediction. Figure 21: SHAP-based ranking of importance of multi-source features. Although TRNet has demonstrated certain advantages in the prediction of TBM surrounding rock grades, there remains untapped potential and significance. Firstly, in addition to the three data sources already mentioned, the incorporation of geological data and other geotechnical parameters is recommended in order to enhance the accuracy of surrounding rock grade prediction under various geological conditions. Secondly, there is potential to enhance the automation level of the interpretation process of the physical exploration data, with attempts to perform automatic feature extraction and modelling analysis of the physical exploration data through intelligent methods, which could further reduce the manpower cost. The exploration of more intelligent fusion analysis methods for multimodal data will be undertaken. Conceptualisation: [PERSON], [PERSON], [PERSON] and [PERSON]; methodology: [PERSON], [PERSON] and S.P. ; validation: [PERSON], X.H., S.P. and W.F. ; formal analysis: [PERSON], S.C. and B.G. ; investigation: S.C. and B.G. ; resources: [PERSON] and W.F. ; data curation: S.C. and B.G. ; writing--original draft preparation: [PERSON] and S.P. ; writing--review and editing: [PERSON], S.P. and W.H. ; visualisation: S.P. ; supervision: [PERSON] and W.H. ; project administration: [PERSON] and B.G. ; funding acquisition: [PERSON] and W.F. All authors have read and agreed to the published version of the manuscript.