Experimental validation of a prediction model of the compressive strength of cemented rockfills
Bibliographic record
Abstract
Underground mine backfilling promotes solid waste to be returned as cementitious material either in the form of cemented paste backfill – CPB – (using tailings) or in the form of cemented rock fill – CRF – (using crushed waste rock, WR). The cement or binder addition is intended to develop a required unconfined compressive strength (UCS) value to ensure ground stability during mining operations. While CPB is the most common type of mine backfill used in underground mining operations, CRF is only used when high compressive strength is required to increase productivity. Despite the performance of CRF, this type of backfill is not much studied or optimised. The main objective of this study is to validate experimentally a newly developed semi-empirical model for predicting the UCS of CRF. This model considers various physical parameters of CRF materials such as the types of binder (e.g. general use Portland cement –GU, GU-fly ash, GU-ground granulated blast furnace slag, etc.) and their mass proportion (binder rate Bw), the water-to-cement ratio (W/C), the type of WR (according to its relative density DR) and the grain size distribution, and the curing time (t). To this end, numerous cylindrical CRF specimens are prepared by varying the W/C, the type of binder, the binder rate Bw (4–8%), the type of WR and the average diameter (d) of the particles. Preliminary results show that the accuracy of the predicted UCS values of various laboratory-prepared CRF mix recipes is satisfactory with a high coefficient of correlation (R 0.9). Therefore, it is reasonable to adopt the proposed CRF strength prediction model for laboratory-prepared specimens that can be scaled up in situ bydeveloping an efficient CRF preparation quality control (QC) procedure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".