Choice of the Arch Yielding Support for the Preparatory Roadway Located near the Fault
Bibliographic record
Abstract
The article presents a method of selecting an arch yielding support for preparatory workings driven in a hard coal seam. Particular attention was paid to discontinuous deformation in the form of a fault, which significantly contributes to the change of the excavation protection schemes. On the basis of the geometry of the machines and devices in the designed excavation, the support was selected, which was then checked for the ventilation criterion. In the next stage, analytical calculations were carried out using the determined spacing of the steel support in the fault zone and the area outside of it. Additionally, using the RS3 numerical software based on the finite element method, a rock mass model with a fault was built, through which the preparatory excavation passes. The aim of the research was to determine the total displacements occurring in the fault crossing zone for the excavation without support and with the use of steel arch yielding and with additional reinforcement in the form of straight segments. In conclusion, it was found that the variants of the excavation reinforcement can be modeled and selected in advance, which allows for the fastest possible execution of the driving and the maintenance of the minimum movement dimensions while passing through the fault.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".