## 4 Case Study
### Discussion  
The results based on the source of the water showed that the water gushing was released primarily from bedrock fractures, but was still weakly influenced by the surface water system. From Figure 9, it was seen that the curve was abrupt and starts with a large value in the first stage. This was the stage where the bedrock fracture water continued to push into the tunnel in large quantities in a short period of time. The site conditions were consistent with those expressed at this stage.  
The second stage curve was smooth. It showed that the bedrock fracture water above the tunnel site area was reduced at this time. Bedrock fracture water and infiltrated rainfall from the adjacent areas of the tunnel site were continuously replenished into the rock above the tunnel site along the fracture structure. The site conditions were also consistent with those expressed at this stage.  
In the third stage, the curve was slowly increasing, which reflected the fact that the water gushing in the tunnel at this stage were influenced by factors, such as rainfall and changes in tectonic fissures. Comparing the left and right halves of Equation (23), the following was obtained:  
\[\frac{W_{n(1)}}{W_{n(2)}}=\frac{12.16\mu H_{n}}{0.0657\epsilon a_{T}}. \tag{26}\]  
Although it was assumed in this paper that the variation in \(H\) was negligible to represent the strong water transport capacity of the bedrock, \(H\) should in practice be decreasing as \(T\) increases. Hence, Equation (24) should be constantly converging to zero with the rest of parameters remaining constant.  
Overall, the impact of water from the bedrock fracture on the water gushing would become smaller with the increase in the duration of the water gushing, and the impact of the infiltration of water from the surface would become greater. The error between the calculated results and the actual monitoring values was within 10%, except for the 1-6 days, when the error in the calculated results ranged from 10% to 20%. In the study of the erosion and seepage process of the surrounding rock and the characteristics of the water intake of the fault fracture zone, other scholars also adopted a three- or four-stage model to use in  
Figure 9: Comparison of calculated results with dynamic monitoring records.  
the study [39; 40]. Therefore, it was reasonable to divide the period of water gushing into three stages in this study.  
Meanwhile, the method of rainfall infiltration and method of the runoff module number of the groundwater were applied for comparison with the methodology of this article. As their approach to prediction differed from the method in this study, some of the results with an average duration of 150 d, 200 d, 250 d, 300 d, and 350 d for predicted water gushing from the hydrogeological investigation phase of this project were selected for comparison, which were shown in Table 2.  
From Table 2, it could be seen that the results from the methodology of this article were closer to the dynamic monitoring data, while the other methods had very different results. It was worth noting that method of rainfall infiltration and method of the runoff module number of the groundwater were not sufficiently concerned with the static water from the bedrock fracture in the prediction of water gushing, and they focused more on rainfall and surface water recharge to groundwater. That was the main cause of the error. On the other hand, the catchment area was not expressed by a fixed value when predicting water gushing. It was related to the changes in the average thickness of the aquifer and the duration of the water gushing. This was another cause of error. In contrast, the approach in this paper was more concerned with the water from the bedrock fracture. The fuzzy clustering method was used to monitor the release of water from the bedrock fracture and the infiltration of rainfall for replenishment. The dynamic effective radius of water gushing was applied to reflect the changes in catchment area, which was why it had fewer errors.