Influence of curing temperature and stress conditions on mechanical properties of cementing paste backfill
Bibliographic record
Abstract
Cemented paste backfill (CPB) has been observed to achieve greater cemented strength when cured in situ compared with equivalent mixes cured and tested in a laboratory environment. This is in part due to the development of effective stress and generation of elevated temperatures by exothermic cement hydration reactions occurring during curing in a typical underground stope environment. This differs from curing in typical laboratory environments, where little or no effective stresses are generated and curing occurs under constant-temperature conditions. This paper outlines the development, calibration, and testing of a temperature-controlled hydration cell that provides closer representation of in situ conditions by controlling the rate and final amount of specimen temperature increase, in addition to curing under effective stress. The temperature-controlled hydration cell was used to examine the effect of curing under combined effective stress and temperature conditions on the development of small-strain stiffness over a 7 day curing period and the unconfined compressive strength at the end of this period. Curing with both elevated temperature and effective stress was found to significantly increase the mechanical properties of CPB compared with curing at elevated effective stress or ambient temperatures alone.
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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) | 0.001 | 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".