## 3 Results  
### Construction Datasets from Field Tests Results  
From the field test, it can be found that the sensors buried in the roof of the structure can obviously reflect the disturbance of vehicle loads during the operation period. Therefore, the results of those sensors are used in this study. From the comprehensive analysis of the engineering condition, site investigation and real measurement data, it can be found that the main load disturbance factors affecting the response of the utility tunnel structure include the vehicle driving speed, the magnitude of the vehicle load, the lateral distance between the vehicle wheel load center and the mid span of the structure roof, the symmetry of the vehicle load distribution and the operating time. The disturbance response of the utility tunnel structure is mainly represented by the settlement and the horizontal stress at the mid span of the structure roof. It must be noted that the settlement of the mid span of the structure roof can be computed by the obtained concrete strain results at the corresponding position, and the horizontal stress at the mid span of the structure roof is also computed by the obtained steel stress results at the corresponding position. Therefore, to predict the safety of the utility tunnel structure, the four vehicle operating load parameters and one operating time parameter are selected as the influence factors, which are the input variables of the prediction model, and the response factors of the utility tunnel structure are the settlement and the horizontal stress at the mid span of the structure roof, which are the output variables of the prediction model. According to the requirements for the input and output variables of the prediction model, field test data have been organized to establish the datasets for the training the model. For the large number of field test datasets, in order to illustrate the form of the dataset, only part of the data are provided here, as shown in Table 1.  
It must be noted that, in Table 1, the \"1\" in the third column represents load symmetry, and \"2\" represents load asymmetry. For the data in each line, the first five ones represent the five disturbance factors, and the last two represent the two corresponding responses of the utility tunnel structure affected by the first five disturbance factors. The data in each line are one training sample of the prediction model, and the datasets of the prediction model are constructed by numerous lines of the training sample.  
Finally, it must be noted that, because the load disturbance factors can be determined easily in the field tests at one engineering site, in this study, only the main load disturbance factors have been considered. Actually, in real engineering, there are other influence factors on the performance of the utility tunnel structure, such as the type of soft foundation soil.  
Figure 9: Layout of monitoring points.  
However, those factors are unchanged for one particular utility tunnel during the operation period. Therefore, those influence factors cannot be taken as the disturbance factors for one particular form of engineering. Moreover, for one particular utility tunnel, the influence of the type of soft foundation soil has been represented in the implicit relationship between the load disturbance factors and structural responses. Therefore, for this prediction study, the influence of the type of soft foundation soil is not considered. That is to say, the type of soft foundation soil does not affect the predictive accuracy of the deep learning model.