Feasibility of backfilling mines using cement kiln dust, fly ash, and cement blends
Bibliographic record
Abstract
Cement kiln dust (CKD) is an industrial by-product of the cement manufacturing process, the composition of which can vary widely. Recent years of using alternative fuels have resulted in higher chloride and alkali contents within CKDs; as such, this limits the applications in which CKDs can be utilised. Using a CKD containing a high free lime content of 29.5%, it is shown that this CKD is capable of activating pulverized fuel ash (PFA) due to its high alkalinity, which can be utilised in low strength un-reinforced applications. One potential application involves the backfill of mines, reducing the need for continuous maintenance of the mine. This study focuses on the compressive strength achieved by various blends of CKD, PFA, and cement. Samples were hand mixed and compacted in 100 mm x 50 mm diameter cylinders, and unconfined compressive strength measurements taken at 28 and 56 days. The hydration products were assessed through the use of x-ray diffraction and thermogravimetric analysis. Aiming to maximise the use of CKD at a water to binder (w/b) ratio of 0.2, it was found that the maximum CKD content possible to achieve the required strength was 90% CKD blended with 10% cement.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".