## 5 Conclusions  
In this study, correlations between geological parameters and tunneling parameters were analyzed based on the field data of the KS Water Conveyance Tunnel in Xinjiang, China, and the water conveyance tunnel project in Lanzhou, China. An empirical SRE classification system was established, and the applicability of the proposed system was verified using engineering examples. An integrated learning model based on prior knowledgewith grid search was proposed to optimize DF and was used as an intelligent identification method for SRE. The performance of various machine learning models was compared, and their influencing features were analyzed. The following main conclusions are drawn.  
(1) The use of \(SER\) to only evaluate SRE can cause certain errors for fractured and poorly integrated surrounding rocks (Classes IV and V). Thus, \(PR\) can be used as another evaluation parameter. This can reduce random errors due to single-index characterization.  
(2) The correlations between evaluation indexes (\(SER\) and \(PR\)) and geological parameters (\(BQ\) values) were analyzed using statistical analysis. The fitted curves with high accuracies (R\({}^{2}\) = 0.8209 and 0.8001) were obtained. Thus, an SRE classification system based on multi-objective decision-making TOPSIS was proposed and validated using 15 samples from the TBM tunneling sections of KS Tunnel. The validation results were consistent with the field excavation situation, confirming the effectiveness of the proposed system. This system can provide a reference for TBM tunnels with the same lithologies.  
(3) Ten TBM tunneling parameters were used as input features of the model. Then, after six iterations of hyperparameter optimization with manual presetting and grid search, the proposed DF-based intelligent identification model for SRE had an accuracy and F1 score of 96.33% and 0.9581, respectively. The proposed model exhibited higher performance than the commonly used DT, RF, SVC, and DNN algorithms. In addition, TBM tunneling parameter data did not strictly follow a normal distribution. Therefore, during outlier processing, using the commonly used \(3\sigma\)-rule can cause some errors. Therefore, this paper used the box plot method to deal with outliers. The K-means algorithm was also used to extract TBM data in the stable phase, with good performance.  
(4) Feature importance was measured using RF. The results show that among the ten input features, penetration and advance rate contributed the most to the model, with contribution scores of 0.2619 and 0.2473, respectively. The top shield cylinder rod chamber pressure contributed the least to model performance, with a score of 0.0268 only.  
Conceptualization, J.Z., K.S. and H.M.; methodology, J.Z., K.S. and H.S.; software, J.Z.; validation, J.Z., R.S., T.F., Z.L. and H.M.; investigation, J.Z. and K.S.; data curation, J.Z., H.M. and Z.L.; writing--original draft preparation, J.Z. and K.S.; supervision, K.S.; funding acquisition, K.S. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention, grant number ZDSYS-YS-2022-08.  
Not applicable.  
The data that support the findings of this study are available upon request from the corresponding author. The data of K.S. Tunnel are not publicly available due to privacy or ethical restrictions. The data of the Lanzhou water conveyance project in China are available in the cited article.  
We would like to thank engineers [PERSON] and [PERSON] at Xinjiang Erzis River Investment and Development (Group) Co., Ltd. for providing the data and thank Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention for funding the test.  
The authors declare no conflict of interest.