A failure criterion for shale considering the anisotropy and hydration based on the shear slide failure model
Bibliographic record
Abstract
A failure criterion fully considering the anisotropy and hydration of shale is essential for shale formation stability evaluation. Thus, a novel failure criterion for hydration shale is developed by using Jaeger’s shear failure criterion to describe the anisotropy and using the shear strength reduction caused by clay minerals hydration to evaluate the hydration. This failure criterion is defined with four parameters in Jaeger’s shear failure criterion (S1, S2, α and φ), three hydration parameters (k, ωsh and σs) and two material size parameters (d and l0). The physical meanings and determining procedures of these parameters are described. The accuracy and applicability of this failure criterion are examined using the published experimental data, showing a cohesive agreement between the predicted values and the testing results, R2 = 0.916 and AAREP (average absolute relative error percentage) of 9.260%. The error (|Dp|) is then discussed considering the effects of β (angle between bedding plane versus axial loading), moisture content and confining pressure, presenting that |Dp| increases when β is closer to 30°, and |Dp| decreases with decreasing moisture content and with increasing confining pressure. Moreover, |Dp| is demonstrated as being sensitive to S1 and being steady with decrease in the data set when β is 0°, 30°, 45° and 90°.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".