## 3 Results
### Analysis of Predication Results  
The training sets are used to train the predication model, and the WO-DBN model can be obtained. The optimized hyperparameters of the DBN, which are the parameters of the WO-DBN, are summarized in Table 3.  
The testing sets have been substituted into the obtained WO-DBN model, and the prediction results of the structure response can be obtained, which are as shown in Figure 11. It must be noted that, for comparison, the real test results are also shown in this figure.  
It must be noted that, in Figure 11, to show the computing errors between the prediction results and the real test ones clearly, the scattered points of vertical displacement and the horizontal stress of the structure roof corresponding to different samples were artificially connected to form variation curves. Therefore, the variation curves in Figure 11 have no practical meaning.  
From Figure 11, it can be found that the prediction results of the WO-DBN model are in agreement with the real test results--that is, the performance of the constructed prediction model in this study is suitable.  
To deeply analyze the prediction results, based on Equations (17)-(19), the quantitative evaluation indexes (RMSE, MAE and R) can be obtained, which are summarized in Table 4.  
From Table 4, it can be found that, for the vertical displacement of the structure roof, the values of RMSE and MAE are all small, and the value of R is 0.9742, which is near 1. Therefore, the prediction results are very good. However, for the horizontal stress of the structure roof, the values of RMSE and MAE are large. The reason is that the computing values of RMSE and MAE are related with the magnitude of the data [27; 35]. Because the magnitude of the data for the horizontal stress is much larger than that for the vertical displacement, there are big differences for the computing values of RMSE and MAE between them. Moreover, the computing value of R is not related with the magnitude of the data [27; 35]. Therefore, its result is relatively effective. Actually, for the horizontal stress of the structure roof, its computing value of R is 0.6825, which is near 0.7, that is, the prediction result of WO-DBN is somewhat suitable too. Obviously, the prediction result of vertical displacement is better than that of horizontal stress, that is, the obtained WO-DBN prediction model has a better predictive effect on the structure deformation response. The reason is that the displacement response of the utility tunnel structure to the disturbances such as operation loads is more obvious than the stress response, which is represented by the more obvious change in results in Figure 11 than that in Figure 11b.  
From the above analysis, it can be found that, based on the constructed WO-DBN model and considering the disturbance factors of the utility tunnel structure during the operation period, the corresponding disturbance response of the utility tunnel structure can be predicted well. Therefore, this study can provide an effective way to dynamically predict the safety response of the utility tunnel structure during the operation period based on the big data analysis by the artificial intelligent method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indexes** & **RMSE** & **MAE** & **R** \\ \hline Vertical Displacement & 0.2312 & 0.1604 & 0.9742 \\ Horizontal stress & 22.0217 & 12.3726 & 0.6825 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation indexes of the WO-DBN prediction model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(n_{1}\) & \(n_{2}\) & \(n_{3}\) & \(n_{4}\) & \(\eta\) & \(t_{1}\) & \(t_{2}\) \\ \hline Values & 44 & 47 & 34 & 18 & 0.0044 & 13 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the WO-DBN model.