Time‐Dependent Failure of Rock: Insights From Grain‐Scale Stress Corrosion Simulation
Bibliographic record
Abstract
ABSTRACT This research investigates the time‐dependent damage deformation of granular rock using a grain‐scale stress corrosion (GSC) modeling approach. Stress corrosion, originating from damage within the grains and along the grain boundaries, is considered the main mechanism causing weakening and time‐dependent damage of granular rock. To account for the microstructure geometry of rock, the parallel‐bond stress corrosion (PSC) model is extended to a grain‐based model (GBM) within the Particle Flow Code (PFC). Accordingly, the modeling parameters are calibrated against data from uniaxial compression and fatigue tests on Lac du Bonnet granite. The numerical modeling results show that the long‐term strength and failure time of granular rock increase prominently with the increase of confining pressure and the decrease of the driving‐stress ratio. The number of microcracks along the grain boundaries is far more than that within the granule interior. Grain crushing, which is traced by granule interior microcracking, appears in specimens with high confining pressures. It is found from the simulation results that the damage in the failed specimens caused by long‐term loading, both inside the grains and along the grain boundaries, is less than that caused by short‐term loading. The results also show that stress corrosion within the granule interior significantly influences the time‐dependent behavior of granular rock under high driving‐stress ratios, whereas stress corrosion along the grain boundary becomes dominant under low driving‐stress ratios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".