Response surface methodology-based characterization and optimization of fibre reinforced cemented tailings backfill with Slag
Bibliographic record
Abstract
With the increasing depths of underground mines due to the scarcity of near-surface ores, the introduction of fibre-reinforced cemented paste backfill (F-CPB) has emerged as a novel solution to address the demanding geomechanical conditions in deep mining operations. However, the widespread adoption of F-CPB in mining and industrial backfill operations necessitates a comprehensive understanding of its key engineering properties, including strength, yield stress, modulus of elasticity, and cost. Moreover, it is crucial to investigate the influence of the constituent materials (water, fibres, binders, tailings) and their interactions on these properties. This research paper presents the application of response surface methodology (RSM) to model the effects of binder content (Portland cement/Slag), water content, fibre content, tailings and their interactions on the mechanical and rheological properties, as well as the cost of F-CPB. Central Composite Design (CCD) experiments were conducted, and a high degree of agreement was observed between the experimental and predicted responses. The RSM approach proves suitable for accurately estimating the responses and assessing the interactions between the model parameters and the properties of F-CPB. Furthermore, a combination of RSM and the desirability approach enables the development of an optimisation tool for F-CPB, facilitating the formulation of optimal backfill mixtures. The results obtained from this study highlight the effectiveness of the combined RSM and desirability approach in F-CPB mix proportioning, offering an advanced engineering approach to F-CPB mix design. The proposed design method has the potential to reduce the laboratory testing protocol required for determining the optimal mix composition.
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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 | 0.001 | 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".