Numerical Analysis of Synergistic Ground Control Efficacy via Integrated Mining-Backfill-Roof Contact in Metallic Deposits
Bibliographic record
Abstract
To investigate the impact of Integrated Mining-Backfill-Roof Contact (IMBR) synergy on strata subsidence in metallic deposits and analyze strata/surface movement patterns, this study enables safe, efficient, environmentally conscious, and sustainable mining development. Focusing on a representative metal mine, we integrated laboratory testing, theoretical analysis, and numerical modeling to determine experimental parameters. Utilizing MIDAS GTS NX, numerical models incorporated four orebody dip angles (30°, 50°, 70°, 90°), five stress release coefficients (20–100%), and contacted/uncontacted conditions to assess IMBR’s control efficacy on surrounding rock stability and surface subsidence. By examining strata/surface movement under variable dip angles and stress release coefficients, displacement control mechanisms were quantified, revealing strata movement evolution principles. Key findings indicate: (1) For all dip angles, the increase rate of displacement progressively intensifies as the excavation stress release coefficient decreases. Notably, at a 30° dipping angle, the most pronounced reduction occurs under declining stress release coefficients, with overall displacement reduction rates reaching 17% for ground surface and 18% for surrounding rock, respectively. (2) Surface displacement impacts intensify as dip angles flatten. (3) Shallower dips induce more pronounced stress disturbance, expanding overburden movement domains and exacerbating surface impacts. Finite element numerical modeling enables accurate and effective analysis of strata and ground movement patterns under varying orebody dipping angles and mining-backfill stress release coefficients. Findings demonstrate that IMBR technology, compared to conventional roof-contacted backfilling methods, achieves timely roof support through immediate backfill-roof contact, significantly reduces overburden fracture propagation depth, and offers valuable insights for controlling surface subsidence in complex mining conditions—particularly for mining under surface 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".