Identification and Evolutionary Characteristics of Major Fractures in Beishan Granite

Abstract

Identification of major fractures is a common problem in underground engineering. Research on the identification and evolutionary characteristics of major fractures are mainly based on high-level waste underground repositories. In this paper, a triaxial acoustic emission (AE) experiment was carried out, and methods such as fractal theory and machine learning were utilized to analyze the AE characteristics during rock failure. The evolution of fracture clusters within the rock was studied, and the AE characteristics of different fracture clusters were analyzed. The results show that as the confining pressure increases, fracture categories reduce, the proportions of major and non-major fractures decrease, and the proportion of outlier fractures increases. During the initial phase of AE, the proportion of major fractures significantly fluctuates, while during the active phase of AE, the proportion of major fracture acoustic emissions generally increases. The proportion of major fracture acoustic emissions remains relatively constant during the calm phase, and in the destructive phase, the proportion of major fractures slightly decreases. The variations in the b-value can be divided into three stages: increase, decrease, and secondary increase. A rock major fracture identification model was established based on BP neural network, and the model’s accuracy rate of major fracture identification was 87.22%.