Many macrocracks are usually generated during the fracturing of rocks. Elucidating the spatial distribution of cracks provides the basis for understanding crack nucleation and fracture formation in rock mechanics. Considering either a single microcrack or all the microcracks provides a limited interpretation of rock mass failure that is often induced by different macrocracks. Here we recognize macrocracks based on a three-dimensional (3D) crack model, implemented using an unsupervised machine learning algorithm and microcrack coordinates. This approach recognized microcracks that coalesce to form a macrocrack in three dimensions. Rock fracturing was performed using a triaxial loading test, and the coordinate data were obtained via the acoustic emission (AE) technique. The results show that the main macrocracks are distributed throughout the whole granite specimen, and smaller macrocracks form near the unloading surface. The AE-recognized crack pattern was found to be consistent with the actual cracks. The adaptability of the proposed method and the potential research and applications were discussed. This approach provides a means to understand the formation and distribution of rock fractures.

中文翻译：

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无监督机器学习的三维裂纹识别

许多宏观裂缝通常是在岩石压裂过程中产生的。阐明裂缝的空间分布为理解岩石力学中的裂缝成核和断裂形成提供了基础。考虑单个微裂纹或所有微裂纹提供了对通常由不同宏观裂纹引起的岩体破坏的有限解释。在这里，我们基于三维 (3D) 裂纹模型识别宏观裂纹，该模型使用无监督机器学习算法和微裂纹坐标实现。这种方法识别在三个维度上合并形成宏观裂纹的微裂纹。岩石压裂采用三轴加载试验，坐标数据通过声发射（AE）技术获得。结果表明，主要宏观裂纹分布在整个花岗岩试件上，在卸荷面附近形成较小的宏观裂纹。发现 AE 识别的裂纹模式与实际裂纹一致。讨论了所提出方法的适应性和潜在的研究和应用。这种方法为了解岩石裂缝的形成和分布提供了一种手段。