Assessment of the self-desiccation process in cemented mine backfills
Bibliographic record
Abstract
During the placement of fine-grained cemented mine backfill, the high placement rates and low permeability often result in undrained self-weight loading conditions, when assessed in the conventional manner. However, hydration of the cement in the backfill results in a net volume reduction—the volume of the hydrated cement is less than the combined volume of the cement and water prior to hydration. Though the volume change is small, it occurs in conjunction with the increasing stiffness of the cementing soil matrix, and the result in certain circumstances can be a significant reduction in pore-water pressure as hydration proceeds. In this paper, the implications of this phenomenon in the area of cemented mine backfill are explored. An analytical model is developed to quantify this behaviour under undrained boundary conditions. This model illustrates that the pore-water pressure change is dependent on the amount of volume change associated with the cement hydration, the incremental stiffness change of the soil, and the porosity of the material. Experimental techniques for estimating key characteristics associated with this mechanism are presented. Testing undertaken on two different cement–minefill combinations indicated that the rate of hydration and volumes of water consumed during hydration were unique for each cement–tailings combination, regardless of mix proportions.
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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 | 0.000 | 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 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".