Bibliographic record
Abstract
Over the last three decades, mine backfilling has progressively integrated into underground mining operations. The high stresses associated with mining at depth in the Canadian Shield, also requires innovative approaches to mine backfilling to withstand the loading both during and after mining operations. Not only new or modified minefill systems are required, but also new techniques are needed to increase the speed of the mining cycle for optimizing the mining operation. Three major purposes of mine backfill are known as (1) providing safe working condition, (2) maximizing ore recovery and (3) improving underground stability. Therefore, mine backfill has contributed greatly to the economics and environmental aspects of mining industry. In order to improve the mechanical behaviour of fill, cementitious materials are used. These cementitious materials are expensive. As a result the consumption of these cementitious materials has to be optimized and minimized in a way that the required strength is met. The objective of this research is to investigate a new type of backfill, which is known as gelfill. Gelfill binders usually consist of alkali activators such as sodium silicate and the other cementitious materials. Sodium silicate has been used in waste treatment and activation of artificial pozzolans such as blast furnace slag and fly ash. The work presented in this thesis is to evaluate the use of sodium silicate in gelfill. Consequently, the influence of mixing time, mixing sequence and curing time are studied on gelfill and silica sand hydraulic backfill. Various tests including unconfined and confined compressive strength were conducted in order to investigate the mechanical behaviour of samples. By conducting mercury intrusion porosimetery (MIP) and scanning electron microscopy (SEM), microstructure and mineralogical properties of specimens were studied. The result of this thesis demonstrates that gelfill compared with silica sand hydraulic backfill has better mechanical properties. In addition, other variables, including: mixing time and sequence, have a significant effect on gelfill.
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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".