## 5 Conclusions  
Rock mass failure in deep metal mines is complex and uncertain in terms of time, space, frequency, and intensity. Considering the complex characteristics of tunnel failure in the Ashele copper mine, a quantitative method to assess risk of tunnel failure was carried out by considering three aspects of tunnel failure (geological conditions, mining disturbances, and MS parameters). These controls were divided into numerous evaluation indexes and implemented in a normal cloud model to predict damage levels.  
1. The evaluation indexes considered here provided different contributions to the tunnel failure model, and any single index did not lead the factor compared to other indexes. Among the nine indexes, the accumulated energy and accumulated events had the largest contributions, while the weight of the buried depth had the smallest contribution. The five largest weights within the model were attributed to MS indexes and accounted for 83.8% of the total weight. This indicates that the MS indexes play a leading role in the risk assessment of tunnel failure.
2. The eigenvalues of the three damage levels predicted within the Ashele mine were defined by using the normalized expectations (_Ex_), which can reduce the range of the comprehensive evaluation value of each damage level and improve the application of the weighted mean method.
3. The accuracy rate of damage level classified by the maximum membership rule was 81.1% in the Ashele copper mine. By considering the fuzzy entropy of the sample, the comprehensive application of the maximum membership rule and the comprehensive evaluation value were used to improve the evaluation accuracy of the tunnel damage level to 86%.
4. Based on the maximum membership rule, the comprehensive evaluation value, and the fuzzy entropy, a quantitative evaluation method for the tunnel damage risk was established in the Ashele Copper Mine. The application results indicate that the assessment results can provide a basis for the control of ground pressure hazards and the optimization of mining process in deep metal mines.  
[PERSON] put forward the concept and wrote the original draft of the manuscript. [PERSON] processed the data further and edited the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] collected the related references and drew the pictures in the paper. All authors have read and agreed to the published version of the manuscript.  
The paper was financially supported by the National Natural Science Foundation of China (51974059) and the Fundamental Research Funds for the Central Universities (N180115010).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Thanks are given to colleagues for their valuable contributions to the project. Finally, the authors would like to thank the editors and reviewers for their valuable comments and constructive suggestions.  
The authors declare that the paper described has not been published before; that it is not under consideration for publication anywhere else; that its publication has been approved by all co-authors; that there is no conflict of interest regarding the publication of this article.