Damage smear method for rock failure process analysis
Bibliographic record
Abstract
Damage smear method (DSM) is adopted to study trans-scale progressive rock failure process, based on statistical meso-damage model and finite element solver. The statistical approach is utilized to reflect the mesoscopic rock heterogeneity. The constitutive law of representative volume element (RVE) is established according to continuum damage mechanics in which double-damage criterion is considered. The damage evolution and accumulation of RVEs are used to reveal the macroscopic rock failure characteristics. Each single RVE will be represented by one unique element. The initiation, propagation and coalescence of meso-to macro-cracks are captured by smearing failed elements. The above ideas are formulated into the framework of the DSM and programed into self-developed rock failure process analysis (RFPA) software. Two laboratory-scale examples are conducted and the well-known engineering-scale tests, i.e. Atomic Energy of Canada Limited's (AECL's) Underground Research Laboratory (URL) tests, are used for verification. It shows that the simulation results match with other experimental results and field observations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".