In situ pressures in cemented paste backfill — a review of fieldwork from three mines
Bibliographic record
Abstract
There is a requirement for better understanding of in situ cemented paste backfill (CPB) behaviour in order to optimise backfilling strategies, i.e. two stage versus continuous pours, pouring rates, time to blast alongside a CPB stope. Furthermore, such data is required to calibrate numerical and analytical models and provide input parameters for laboratory testing of CPB. In response to the requirement for in situ data, the University of Toronto has led an international research project in which six stopes at three partner mines have been comprehensively instrumented with geotechnical instrumentation. The aim of this project is to better understand CPB in situ behaviour in terms of quantifying cement hydration rates, consolidation and arching mechanisms, to provide some estimates of in-stope and barricade pressures and to ultimately improve backfilling efficiency at the partner mines. In this paper, we firstly review the instrumentation procedures to provide guidance for operations seeking to apply similar site-specific backfill investigation. Secondly, key results are presented from the field tests. Each site presents significant similarities and differences in terms of backfill pressure. New data are presented from Barrick’s Williams Mine, in which low barricade pressures are measured when continuously backfilling a 50 m high, 70° dipping stope. The potential for increasing barricade pressures when flushing paste lines into the stope is demonstrated. Case studies from Inmet’s Cayeli Mine provide a comparison of factors affecting backfill pressures, in terms of the relationship between backfill rise rate and cement hydration rates in controlling barricade pressure. The Xstrata Copper Canada Kidd Mine case studies provide evidence that pressures can increase during periods of downtime in backfilling, which we interpret due to thermal expansion of CPB. Pressures exceeding 1 MPa are measured in the Kidd CPB, as the effect of rockbursts and nearby mining are shown to transfer pressure onto the backfill. At all the sites, the exceptional value of instrumentation in backfilling has been demonstrated. The most efficient backfilling strategies will employ real-time barricade monitoring to backfill stopes on a case-by-case basis.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".