Control effect of coal mining solid-waste backfill for ground surface movement in slice mining: A case study of the Nantun Coal Mine
Bibliographic record
Abstract
Abstract Management of solid waste and protecting the ecological balance of the region are key challenges that the coal mining industry has to face. This study evaluated the effect of solid waste backfilling mining on the overlying strata movement and surface deformation variation pattern in slice mining. The mechanical characteristics of different cemented paste backfills (CPB) were compared. The CPB specimens were made of coal gangue and cement with or without the addition of fly ash. The experiments showed that the mechanical strength of the CPBs made of coal gangue and cement increased dramatically. A numerical simulation was then performed to analyze the variation patterns of the overlying strata displacement and surrounding rock stress distribution before and after filling the 3lower and 3upper coal seams with CPB. The CPBs reduced the movement of the surface by 95.1% and 95% during the mining of the 3lower and 3upper coal seams, respectively. Finally, we used a mining-induced subsidence prediction and analysis system to predict the influence of the 3lower and 3upper coal seams on the ground surface subsidence. It was found that the ground surface subsidence induced by CPB mining was 1/20 that of the cumulative ground surface subsidence caused by caving mining. CPB mining could effectively control the ground surface subsidence caused by multi-slice mining of the thick coal seam, offering protection for buildings above the ground. Our research provides theoretical and technical support for coal mining under buildings subjected to similar conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".