An Investigation of the Uniaxial Compressive Strength of a Cemented Hydraulic Backfill Made of Alluvial Sand
Bibliographic record
Abstract
Backfill is commonly used in underground mines. The quality control of the backfill is a key step to ensure it meets the designed strength requirement. This is done through sample collection from the underground environment, followed by uniaxial compression tests to obtain the Uniaxial Compressive Strength (UCS) in the laboratory. When the cylindrical cemented backfill samples are axially loaded to failure, several failure modes can be observed and mainly classified into diagonal shear failure and axial split failure. To date, the UCS obtained by these two failure modes are considered to be the same with no distinction between them. In this paper, an analysis of the UCS results obtained on a cemented hydraulic backfill made of alluvial sand at a Canadian underground mine over the course of more than three years is presented. The results show that the UCS values obtained by diagonal shear failure are generally higher than those obtained by axial split failure for samples with the same recipe and curing time. This highlights the importance of making a distinction between the UCS values obtained by the two different modes of failure. Their difference in failure mechanism is explained. Further investigations on the sources of the data dispersion tend to indicate that the UCS obtained by laboratory tests following the current practice may not be representative of the in-situ strength distribution in the underground stopes due to segregation in cemented hydraulic backfill.
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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 | 0.001 | 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) | 0.001 | 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".