## 5 Conclusions  
Most of the extensively hidden underground tunnels in Kinmen Island have been abandoned and unoccupied to avoid the damage to human life and property caused by tunnel breakdown and prevent accidental destruction of the precious cultural heritage. This study used 2D-ERT for in situ measurement to find the location of tunnels hidden underground. To test the reliability of 2D-ERT in tunnel survey, a numerical model was built. The actual configuration of the onsite strata and the shape of the underground tunnel were simulated to analyze and validate the accuracy of in situ measurement. In addition, to enhance the measurement result interpretation ability and resolution to determine the actual position and path of the tunnel, this study used an artificial intelligence algorithm to estimate and build a 3D electrical resistivity model.  
The results indicate that three survey lines were used for 2D ERT measurement in this study. As the tunnel is a reinforced concrete structure, it is characterized by unlikely electrical conduction and high resistivity, especially in the 2D ERT result. The three survey lines have high resistivity regions in the profiles. According to the numerical simulation result, this high resistivity region is highly likely to be the tunnel location, and the tunnel location, height, and width have been displayed clearly. The three survey lines are displayed in 2.5D mode. The high resistivity regions of various profiles are selected and connected in line, and the selected region and path are similar to in situ tunnel location and path. According to the results of this study, using 2D-ERT to survey underground tunnels is highly feasible.  
With the 2D-ERT test result showing a 2D profile, the tunnel location and path cannot be interpreted directly. As the 3D electrical resistivity survey is limited by space, time, and economy, the measurement is not yet frequently used. In recent years, artificial intelligence has been extensively used in various domains. To establish a 3D resistivity map, this study used the MLP of artificial intelligence deep learning algorithm for calculation and used the computational ability of learning, data prediction. The error between the actual value and prediction value of MLP was reduced to complete the building of in situ 3D electrical resistivity model. According to a series of findings, the tunnel location and path can be displayed using an artificial intelligence algorithm. The measurement result interpretation ability and resolution are enhanced effectively. This study can further discuss different types of tunnels in the future to enhance the feasibility of 2D-ERT for underground tunnel surveys.  
Conceptualization, Y.-C.H.; methodology, Y.-C.H. and Y.-X.Z.; modelling, W.-C.H.; software, Y.-X.Z. and W.-C.H.; data analysis, Y.-X.Z., W.-C.H. and Y.-C.H.; conclusions, Y.-C.H. and Y.-X.Z.; Field test, W.-C.H. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Project number MOST 109-2221-E-507-004.  
Not applicable.  
The authors declare no conflict of interest.