Integrated analysis of crack evolution in anchored jointed rock using digital image correlation and deep learning-based detection

Accurate characterization of fracture evolution in jointed rock is essential for understanding its mechanical behavior and failure mechanisms. This study proposes a unified framework that integrates Digital Image Correlation (DIC) and deep-learning-based crack detection to quantitatively link deformation localization with crack evolution in anchored jointed rock. Uniaxial compression tests were conducted on rock-like specimens with different joint inclinations and anchoring conditions to capture full-field deformation and fracture responses. DIC results indicate that anchoring conditions significantly influence strain localization and crack propagation. Compared with unanchored specimens, both free and restrained anchoring reduce strain concentration, with restrained anchoring exhibiting a stronger suppression effect and delaying crack coalescence. To enable automated crack identification, an improved YOLOv8n-DWConv-C2FN model was developed and trained using image data derived from DIC-monitored experiments. The model provides quantitative crack localization information through bounding boxes, enabling the extraction of crack distribution and morphology features. By combining the detected crack regions with DIC-derived strain localization fields, spatial consistency metrics (R 1 , R 2 ) and a composite index (CPCA) were proposed to quantitatively evaluate their correspondence. The results demonstrate that the proposed approach effectively correlates deformation localization with crack evolution, with higher consistency observed for dominant crack propagation paths. The proposed methodology provides a quantitative pathway for integrating deformation-field measurements with data-driven crack recognition, enabling more reliable fracture monitoring and analysis in rock mechanics and geotechnical engineering.