## 3 Results
### Process of Prediction by WO-DBN  
The prediction process of the settlement and the horizontal stress at the mid span of the structure roof based on WO-DBN is shown in Figure 10.  
From Figure 10, the computing process of the prediction structure response of the utility tunnel is as follows.  
(1) Based on the field test results, the datasets for the safety prediction model of the utility tunnel structure during the operation period have been constructed, that is, the field test datasets for the response of the utility tunnel structure during the operation period (including the structure deformation index data and structure stress index data) have been selected. Here, the field test data of a one-year simulated operation period have been used.  
(2) The field test datasets have been preprocessed. The duplicate data and obvious abnormal data have been removed. The zero values in the datasets have been revised to the very small values (greater than zero). After preprocessing, a total of 15,376 sets of valid data have been obtained.  
It should be noted that, if the amount of training data is very huge, to reduce the workload of processing the training data, the digital signal processing methods such as the wavelet analysis or filtration methods can be employed to optimize the selection and preprocessing of training data.  
(3) Considering the total amount of data, the datasets have been divided. Approximately 80% of the data have been selected as the training sets, including a total of 12,110 sets, and the remaining data are as the testing sets. The used datasets are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Input Variables**} & \multicolumn{4}{c}{**Output Variables**} \\ \hline
**Operating** & **Vehicle Speed** & **Symmetry of** & **Magnitude of** & **Load Position** & **Vertical** & **Horizontal** \\
**Time** & **/km/h** & **Vehicle Load** & **/km** & **/m** & **Displacement** & **Stress** \\
**/h** & & & & **/km** & **/km** & **/km** \\ \hline
1 & 22.5 & 1 & 156.49 & 5.25 & 2.0053 & 720.342 \\
1.6 & 41.25 & 1 & 169.17 & 5.25 & 2.2926 & 730.722 \\
2.17 & 60 & 1 & 190.01 & 5.25 & 2.2596 & 725.702 \\
1.26 & 60 & 1 & 195.81 & 5.25 & 2.4146 & 728.099 \\
4.31 & 41.25 & 1 & 180.01 & 5.25 & 2.0452 & 720.963 \\
4.63 & 80 & 2 & 90.01 & 6.39 & 1.8 & 391.573 \\
1.17 & 41.25 & 2 & 81.25 & 7.34 & 1.17 & 303.95 \\
4.06 & 80 & 2 & 100.1 & 6.39 & 2.24 & 383.41 \\
1.25 & 80 & 2 & 96.58 & 6.39 & 2.76 & 340.47 \\
1 & 41.25 & 2 & 96.58 & 7.34 & 2.16 & 288.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Part data of field test datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Field Test Datasets** & **Training Sets** & **Testing Sets** \\ \hline Number of sets & 15,376 & 12,110 & 3266 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The used datasets.  
(4) The hyperparameters of the DBN have been optimized by the WOA, and the prediction model by the DBN based on the optimization results has been established. The predication model has been trained by the training sets to determine the parameters of the predication model, and the WO-DBN model for the prediction of the vertical displacement and the horizontal stress at the mid span of the structure roof can be obtained.  
(5) The data of the load and operating time of the testing sets have been substituted into the obtained WO-DBN model, and the corresponding structure response can be obtained, which are the predicted results of the vertical displacement and the horizontal stress at the mid span of the structure roof.