Revisiting the Brazilian disc test with split Hopkinson pressure bar by high-speed digital image correlation analysis
Bibliographic record
Abstract
The ISRM-suggested Brazilian disc (BD) test using split Hopkinson pressure bar (SHPB) for dynamic rock tensile strength requires central crack initiation and stress equilibrium. This study aims to re-evaluate the critical strain rate, ensuring a valid dynamic Brazilian disc test, and to analyse the reliable dynamic tensile behaviour of granite using high-speed digital image correlation (DIC). The comparison between the measured strain obtained through high-speed DIC analysis and the strain gauge allowed for determining the optimal subset parameters used to obtain the real-time deformation field and the stress-strain curve from DIC data. Crack initiation, crack velocity, and failure process are studied to reveal the rate dependence of granites. A unified dynamic increase factor (DIF) model is proposed for the tensile strength of rocks, and the reason for the sudden drop in DIF for high strain rates is discussed. The results reveal that the upper limit of the valid strain rate, which ensures the validity of the ISRM-suggested dynamic BD test, is co-determined by the conditions of stress equilibrium and crack initiation from the centre of the disc. At higher strain rates (75 s -1 ), BD test results fail to capture the actual tensile behaviour of rocks, and the potential factors influencing the critical valid strain rate (CVSr), such as sample radius and boundary crack length, should also be considered.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".