Mechanical properties and mesoscopic analysis of rockbackfill-rock composite sample under dynamic loading
Bibliographic record
Abstract
During ore pillar excavation, the impact load of deep hole blasting does not act directly in the cemented paste backfill (CPB), which first acts within the ore pillar and then transfers the blast energy to the CPB and the adjacent ore. Therefore, it is a key issue to investigate the stability of the rock-backfill composite under dynamic loading. The paper prepared rock-backfill-rock (RBR) composite samples and conducted the Split Hopkinson Pressure Bar (SHPB) dynamic test with different impact amplitude. The dynamic uniaxial strength characteristics, mechanical characteristics, and failure modes of RBR composite samples under different impact velocities were analysed. The experimental results showed that the dynamic uniaxial compressive strength of the RBR sample increases first and then decreases with the increase of the average strain rate. With the rise in the average strain rate, the fragmentation degree of the RBR sample is deepened. According to the computerised tomography (CT) test results, the rock fails mainly in shear. Moreover, damage occurs at the interface between the CPB and the rock near the incident bar. It is primarily manifested as a ring-layered crack, resulting in its separation from the rock near the incident bar. The results of the study can further guide the stability of the backfill stope during the ore pillar extraction process.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".