Use of Machine Learning for Determination of Deformation Silica Sand Quartz Particles

Grain breakage occurs in sand specimens subjected to high stress levels; however, the magnitude and characteristics of the resulting deformation remain insufficiently quantified. This study investigates particle-scale fracture behavior in a standardized quartz sand subjected to controlled mechanical loading. Rapid, unconsolidated–undrained (UU) direct shear box tests were performed under normal stresses of 700, 800, and 900 kPa to induce grain breakage. The mechanical loading procedure was applied as a controlled stress induction mechanism to promote particle fragmentation rather than to determine conventional geotechnical parameters. A uniformly prepared quartz sand containing no additional mineral phases was used to ensure material consistency. Post-test specimens were examined through systematic visual and image-based analysis. The sample obtained from the 900 kPa test, where breakage was most pronounced, was analyzed in detail to characterize quartz fracture behavior under compressive and shear stress conditions using advanced image processing techniques. A deep learning-based mineral segmentation framework was developed using a ResNet50 architecture with transfer learning. A custom dataset consisting of high-resolution mineral images and corresponding pixel-level segmentation masks was constructed. The proposed model achieved 86.21% overall accuracy, a Dice coefficient of 91.35%, and an Intersection-over-Union (IoU) score of 84.07%. Validation results demonstrated strong generalization capability, with validation accuracy, Dice score, and IoU of 87.47%, 90.07%, and 81.96%, respectively. The high-precision segmentation performance enabled a comprehensive fracture analysis of 3333 quartz mineral images obtained from specimens exposed to systematic stress conditions.