Establishment of a Dynamic Mohr–Coulomb Failure Criterion for Rocks
Bibliographic record
Abstract
Abstract Static Mohr–Coulomb Failure Criterion for rocks has been used extensively in various rock engineering applications. In this model, the compressive strength, tensile strength, and shear strength are related. To investigate the applicability of the Mohr–Coulomb model to dynamic failures, we studied the correlation of the three dynamic rock properties: compressive strength, tensile strength and punch shear strength. The strengths are quantified using a split Hopkinson pressure bar (SHPB) system. The methods for acquiring these strengths were briefly discussed. A fine-grained sandstone, Longyou sandstone (LS) was studied using these methods. The results showed that the compressive strength calculated from the punch shear strength of LS matched well with the experimental UCS results, thus the punch shear strength could be effectively used to predict the UCS of rocks. The tensile strength calculated from the punch shear strength of LS also has exhibited a good trend with the experimental results of Brazilian tensile strength. We concluded that the famous Mohr–Coulomb criterion can be extended to the dynamic range. However, caution has to be taken in determining the loading rates for different strength results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
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".