2022 Volume 10 Issue 1 Pages 260-274
The design of support structures for Japanese mountain tunnels is difficult owing to the limitations in investigating techniques and the complexity of the geological structures involved. In general, during excavation, the size of a support structure is modified to best suit the geological conditions. As such, engineers must carefully observe the rock mass conditions to understand their mechanical and hydraulic properties as accurately as possible, and to predict their deformation behavior and hydraulic structure. In recent years, artificial intelligence (AI) has been adopted in various fields. In this study, a deep neural network (DNN) was applied to evaluate a mountain tunnel’s rock mass. The input was a photo of the excavation surface (face) of the mountain tunnel, and the output was the rock mass properties such as degree of weathering, alteration, and fracture. Based on past excavation records, the DNN was tested using supervised learning, and the results showed that the AI judgments were consistent with the engineers’ judgments, having a 73%–97% accurate answer rate. Therefore, practically applying the method of rock mass evaluation using AI was determined as being feasible. Furthermore, to allow ease in its field-based application, a cloud computer system using a tablet computer device was used to enable evaluations, creating a system that contributed to increased productivity.
