## 3 Results and Discussion
### Reliability Analysis  
To verify the reliability of the 2D-ERT images, this study constructed a numerical model to validate the correctness of in situ measured data. To simulate the onsite strata condition, this study used the same measurement parameters as in situ measurement, referred to the geologic drilling data of site, and took the silty sand as background to build the numerical model of a tunnel to simulate the stratum tectonics of the tunnel in the stratum. Referring to the site concrete tunnel size, a 3 m high and 5 m wide square tunnel was built at 2 m underground, and the reasonable resistivity of each unit was assumed. The resistivity value of the concrete tunnel was assumed to be 1000 ohm-m, dry air inside a tunnel, the resistivity was assumed to be 2000 ohm-m, the resistivity of the soil layer covering the tunnel was assumed to be 400 ohm-m. The numerical model is shown in Figure 7.  
Figure 8 shows the numerical simulation result (RMS Error = 0.46%). As seen, the background silty sand shows low resistivity distribution, and relatively low resistivity values are shown at 2-18 m and 26-42 m of the survey line. Relatively high resistivity distribution is shown at 20-25 m of a survey line and 2-5 m of depth, indicated by red dotted lines in Figure 8. The numerical simulation result in Figure 8 is compared with the in situ test results in Figures 3-5. The four figures are very similar to each other, proving that the stratum tectonics in Figures 3-5 match the stratum tectonics assumed by numerical simulation, proving high reliability. Therefore, the high resistivity region is identified as the tunnel location and path. Figure 9a-g shows a series of results of deep learning prediction. Before artificial intelligence calculation, the measured data in this research must be converted into an MLP data format. The L1, L2, and L3 in situ measurement results are inputted to establish the graph of the relation of relative positions of L1, L2, and L3 for the initial model. Figure 9a shows the initial model of the original data of L1, L2, and L3; the result is similar to Figure 6. Table 1 shows the MLP network structure parameters. The MLP is a three-layer architecture, including two hidden and one output layer. Figure 9b shows the training result of the MLP network. The MLP is reiterated during training to reduce the error amount of output and prediction results and performs calculations until the network converges. Through the nonlinear iterative calculation of the MLP, as seen in Figure 9b, the error value is converged slowly after each iterative computation. Figure 9c shows the result of the original data after MLP data prediction. This is the output of the training data, which can be compared with the original data in Figure 9a. It can be seen that the training results can faithfully present the original data characteristics. Figure 9d shows the results of using the trained MLP to predict the unknown regions of L1, L2, and L3. Figure 9d shows the initially blank areas and without onsite measurement data, such as the lower left corner and the lower right corner, which can be predicted by the MLP network to predict the resistance value. Figure 9e shows the result of using the trained MLP to predict successive profiles. When the predicted number of profiles increases, the 3D resistivity model is built, and 14 profiles are predicted in this study. Due to the narrow scope of this study, the 14 layers of data are predicted by three layers of original data of the resistivity values predicted by the MLP network, which is sufficient to establish a 3D model. Figure 9f shows the 3D resistivity model result. The tunnel is a reinforced concrete structure; it is characterized by unlikely electrical conduction and high resistivity, especially in the 2D ERT result. In order to highlight the location of high resistivity, this study concealed the low resistivity colour of the 3D resistivity model in Figure 9f.