Asymmetric Dynamic Support for Roadways in Subvertical Coal Seams
Bibliographic record
Abstract
Due to the unique conditions of the Wudong subvertical coal seam, damage to the underground roadway occurs frequently during the coal mining process. With the increase in mining depth, the energy and incidence of microseismic events increase significantly, showing characteristics of zoning and grading along the strike. Therefore, the damage to the underground roadway of the Wudong coal seam was studied with a focus on dynamic load disturbance. The results showed that the underground roadway damage exhibits asymmetric characteristics, with significant sinking on the north shoulder and various degrees of bulging on both sides of the roadway. The existing support scheme in the mine could not effectively control the deformation and damage to the rock surrounding the underground roadway under the action of dynamic loads. Therefore, the asymmetric support scheme with the high prestressed anchoring long and short anchor cable network was proposed to provide targeted reinforcement for the underground roadway, which has been shown to effectively mitigate the deformation of the surrounding rock. Eventually, considering the characteristics of microseismic event distribution and the observed increase in the rock damage with the increase of the seismic source energy, a segmented and asymmetric support scheme was suggested to tactically support the underground roadway. The findings of this study could potentially improve the ground safety of underground roadways under similar mining conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".