Abstract
In recent years, artificial intelligence (AI) has been adopted in various fields, not only in the industrial scene. 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.
