Understanding the mineralogical heterogeneity and fracture features of granite is essential for evaluating its strength and fracture evolution under load. However, experimental methods for quantitatively characterizing these features from a 3D perspective remain limited. This study presents a quantitative 3D analysis framework incorporating in-situ X-ray micro-computed tomography (micro-CT) scans and a hybrid digital image processing (DIP) technique. Minerals and fractures were identified using the DIP technique that combines a deep learning model, Residual Network-Visual Geometry Group 16-UNet (Res-VGG16-UNet), with a circular threshold segmentation method. The DIP methods also facilitate the classification of fracture types, distinguishing between inter- and intra-granular fractures within minerals. Based on the DIP results, a quantitative 3D analysis of fracture characteristics accounting for mineralogical heterogeneity was conducted. The results illustrate that the DIP method effectively identifies minerals and fractures in CT images, overcoming uneven brightness issues caused by beam hardening artifacts. Across samples, biotite varies most in content (up to 44.76%), followed by plagioclase (19.91%), quartz (8.19%), and K-feldspar (8.21%), indicating that K-feldspar and quartz are more uniformly distributed within the granite block compared to biotite and plagioclase. Furthermore, as biotite distribution expands, particularly within 10% to 40% of the specimen diameter, the UCS of granite decreases. Inter-granular fractures typically initiate during the early stages of loading, while intra-granular fractures emerge under higher stress levels. At the initial loading stage, fractures also tend to propagate toward biotite-rich regions. These findings advance experimental methodologies for investigating rock mechanical behaviors by providing a framework that explicitly incorporates 3D internal structural evolution and mineral-controlled fracture mechanisms.
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He et al. (2025) studied this question.
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