Application of Digital Image Correlation Technique for Measurement of Tensile Elastic Constants in Brazilian Tests on a Bi-Modular Crystalline Rock
Bibliographic record
Abstract
Abstract Most rocks show different elastic properties in tension and compression (bi-modularity). In this study, we derive new equations to calculate the tensile Young’s modulus and Poisson’s ratio from a Brazilian test by incorporating bi-modularity into stress–strain equations. We tested Brazilian specimens of Lac du Bonnet granite and compared the Young’s modulus and Poisson’s ratio in tension obtained with the values from direct tension tests on dog-bone shaped specimens. Digital image correlation (DIC) and conventional strain gauges were used to extract the strain on the flat surfaces of the Brazilian disks. With the DIC technique, a speckle density of approximately 250 speckles/cm2 was needed to accurately capture the strain pattern. The strain measured using the DIC technique was consistent with the strain measured using the conventional strain gauges. The major advantage of the DIC technique is the ability to map the complete strain pattern over the surface of the Brazilian disk and quantify the uniformity of loading. The Young’s modulus and Poisson’s ratio in tension were obtained using the new equations and were found to be in general agreement with the values obtained from the direct tension tests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".