Laboratory investigation into the compressive strength of cemented paste tailings aggregate fills
Bibliographic record
Abstract
The use of backfill has now become a component of underground mining operations. In terms of mining methods, the top-down method would be less expensive than the bottom-up method, irrespective of the rock mass hardness. However, the implementation of the top-down method requires a high compressive strength ( 4 MPa) of backfill. The addition of aggregates (e.g. crushed waste rock) to the tailings could achieve this targeted strength. This paper investigates the effect of adding aggregates (crushed waste rock) on the compressive strength development of cemented paste tailings aggregate fill (PAF). Two types of waste rock were crushed to two aggregate sizes (0/10 and 0/15 mm) and their proportion in PAF mixtures varied from 10 to 50 %v/v (by cumulative volume of dry crushed waste rock and tailings). These are oxidised acid generating waste rocks (AG) and non-acid generating waste rocks (NAG). The binder type used is a blend of 20% general use Portland cement (type GU) and 80% of ground granulated blast furnace slag (GBFS) at 5 wt% (by total dry mass of aggregates and tailings). The unconfined compressive strength was determined at seven, 28 and 90 days of curing. The results show that the addition of crushed development waste rock to the cemented paste backfill increases significantly its compressive strength. The strength gain varies in the range 2893% at 28 days and 544% at 90 days of curing in drained conditions. However, the strength development is influenced by the aggregate class of size, the volume and the mineralogy of the aggregates in the mixture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".