Evaluation of Damage Stress Thresholds and Mechanical Properties of Granite: New Insights from Digital Image Correlation and GB-FDEM
Bibliographic record
Abstract
We employed a novel combination of digital image correlation (DIC) and grain-based hybrid finite-discrete element method (GB-FDEM) to improve the comprehension of the relationships between microstructural features and the mechanical properties of granitic rocks. DIC and numerical results showed that macrocracks initiated and propagated along grain boundaries among different minerals driven by the high stiffness contrast between the compliant biotite and the stiffer feldspar/quartz grains. Surface deformation analyses revealed that tensile-dominated macrocracks open at monotonically increased rates before the crack damage threshold, and the opening accelerated afterwards with the increased shear component. The onset of the acceleration of the opening rate of macrocracks can be used to infer the crack damage threshold. Both strain and acoustic emission were used to infer damage stress thresholds in the synthetic numerical samples. Numerical results showed that the damage stress thresholds and uniaxial compressive strength decrease with increasing grain size following log-linear relations. Coarse-grained samples tend to fail by axial splitting, while fine-grained samples fail by shear zone formation. Biotite and quartz contents significantly affect mechanical properties, while quartz to feldspar ratio is positively related to the mechanical properties. Our study demonstrates the capacities of DIC and GB-FDEM in inferring damage conditions in granitic rocks and clarifies the microstructural control of the macroscopic mechanical behaviors. Our results also provide a comprehensive understanding of the systematics of strain localization, crack development, and acoustic emission during the rock progressive failure process. Supplementary Information: The online version contains supplementary material available at 10.1007/s00603-024-03789-7.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".