Predicting the temperature and strength development within cemented paste backfill structures
Bibliographic record
Abstract
In this paper, a thermo–chemo–mechanical (TCM) model is developed and implemented into FLAC software for predicting the strength development and distribution within hydrating cemented paste backfill (CPB) structure, temperature development and distribution within the CPB structures, heat transfer between CPB structures and mine rock temperatures. The developed model is validated against results from laboratory (on CPB samples and small scale model) and field studies of CPB. The validation tests show good agreement between the predicted and experimental laboratory and field results. The developed tool is then used to simulate the performance of CPB structure in several practical cases of mine backfill operations. From the simulation results valuable information was gained regarding the strength and heat development within CPB structures, the optimal curing time of a given CPB structure, the effect of filling rate and stope geometry of the strength and heat development within CPB structures, the effect of mix design (e.g. %binder, water content, initial mix temperatures, tailings types) on the performance (mechanical, thermal) of CPB structures. The developed tool will contribute to more cost-effective and safer design of CPB structures.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".