Bibliographic record
Abstract
Conventional Mohr-Coulomb model and strain-softening based Mohr-Coulomb model often take an assumption of constant dilation angle,which can not be supported by experimental data,and it is observed that the approach is not successful in characterizing the nonlinear deformation behaviors of rocks. Based on published data acquired from modified triaxial compressive tests with volumetric strain measurement performed on seven types of rocks,a mobilized dilation angle model which considers both confining stress and plastic shear strain is developed. Based on the model response and in combination with the grain size description and the uniaxial compressive strength of the seven types of rocks,the model is generalized for four rock types:coarse-grained hard rock,medium-grained hard rock,fine-medium-grained soft rock,and fine-grained soft rock. According to the principle of nonassociated flow rule in the strain-softening models in FLAC,the relationship between the plastic shear strain in the proposed dilation angle model and characteristic plastic parameter in strain-softening model is deduced;and the proposed dilation angle model is implemented in FLAC using FISH language. The dilation angle model is used to predict the volumetric-axial strain relationships of Moura coal;and the results are found to be in good agreement with experimental results. Finally,the importance of using the confinement and plastic shear strain dependent dilation angle model for rock engineering applications is discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".