Identification and Evolutionary Characteristics of Major Fractures in Beishan Granite

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...

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Bibliographic Details
Published in:Applied sciences 2023-09, Vol.13 (18), p.10355
Main Authors: Wang, Chaosheng, Wan, Hao, Ren, Weiguang, Ma, Jianjun
Format: Article
Language:English
Subjects:
Acoustic emission testing
BP neural network
Cracks
DBSCAN
Deformation
Energy resources
Engineering
Experiments
Geology
high-level radioactive waste
identification
Load
Machine learning
major fractures
Mechanical properties
Nuclear energy
Radioactive wastes
Rocks
Sensors
Waste disposal
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Summary: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%.
ISSN:2076-3417
2076-3417
DOI:10.3390/app131810355