Stability analysis and strength optimization of cemented tailings backfill in high-temperature mining
Bibliographic record
Abstract
To investigate changes in stope stability and cemented tailings backfill (CTB) strength during deep metal mine mining using the filling method in a high-temperature environment, this study analyzed the temperature and mechanical characteristics of the stope via numerical simulations. The optimal CTB mix ratio at different mining depths was determined, and the corresponding safety control measures for deep metal mine filling mining were proposed. Results show that the coupled effect of the temperature field and hydration heat release during deep filling mining complicate the stope environment. Owing to the difficulty in heat dissipation in the middle of the bulk CTB in the stope, the internal temperature of the CTB increases significantly within a short duration. Moreover, the rapid heat conduction around the CTB caused by its direct contact with the surrounding rock causes the temperature field to distribute from the center to the periphery. Throughout the sublevel mining process, the CTB temperature field exhibits the following change pattern: stable → rapidly increasing → slowly decreasing → rapidly increasing → slowly decreasing to stable → slowly increasing → slowly decreasing to stable. A comparison of test results obtained from pillar mining simulations show that the coupled temperature–stress model exhibits greater stability and safety than the single mechanical model. The CTB provides better support under the coupling effect, thus enhancing its mechanical properties under high temperature. A safety factor is introduced for the quantitative analysis of slope stability. The optimal CTB mix ratio at different mining depths is determined via safety factor iteration and economic comparison analysis. Subsequently, a reasonable temperature control scheme was designed, which offers insights into high-efficiency mining while ensuring CTB stability under high temperature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".