Damage evolution law on the surface field of argillaceous dolomite based on Brazilian test and 3D digital image correlation
Bibliographic record
Abstract
This paper aims to disclose the damage evolution law of the surface field of argillaceous dolomite. For this purpose, the 3D digital image correlation was combined with the Brazilian test into a new analysis method for the surface field damage evolution of the rock. First, the stress-strain curve of argillaceous dolomite was obtained in Brazilian test, and the strain contours of the key points were acquired by 3D digital image correlation. Then, the standard deviations of the x and y direction strains at the key points during the test were calculated using statistical methods. In addition, the dual damage factor was introduced to quantify the law of the strain statistics and plotted into a curve. Finally, the strains in x and y directions on the horizontal axis ox in the disc center were obtained through elastic mechanical analysis and compared with those measured by 3D digital image correlation in the elastic phase. In this way, the following conclusions were drawn: the argillaceous dolomite exhibited obvious non-homogeneity in the surface field damage evolution. The different phases of the Brazilian test can be determined accurately according to the turning points of the damage factor curve. The fluctuations of the damage factor curve also reveal the features of surface field damage evolution of argillaceous dolomite in Brazilian test. This research shows that the traditional assumption of homogeneity cannot reflect the heterogeneity of the surface field damage evolution of argillaceous dolomite in Brazilian test and provides a quantitative research method for rock damage evolution in that test.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".