Deformation and Failure Characteristics of Sublevel Tunnels under Triple-Level Combined Backfill Mining in Steeply-Dipping and Ultra-Thick Orebodies
Bibliographic record
Abstract
Under triple-level combined backfill mining (TLCBM), sublevel tunnels in steeply-dipping and ultra-thick orebodies experience severe deformation and failure, posing significant safety challenges. Based on the field investigation, deformation monitoring, stress measurement, and numerical calculation, this study analyses the deformation behaviour, surrounding rock failure, and stress distribution of sublevel tunnels. A theoretical model is developed to reveal the deformation and failure mechanisms, and targeted engineering countermeasures are proposed. Results show that tunnels deform rapidly and continuously, with damage mainly occurring in the sidewalls and roof. The tunnel cross-section evolves into an asymmetric butterfly shape. The probability and intensity of future deformation and failure are positively correlated with the current damage state. Damage exhibits spatial variability: the near-disturbed side is more affected than the far-disturbed side; vertically, the middle tunnel is the most damaged; horizontally, tunnel ends are more affected than the middle. The damage process follows nine distinct stages. TLCBM places tunnels in a high-stress environment and shifts the highest stress from the lower to the middle sublevel tunnel. Disturbed stress is the dominant factor driving tunnel damage. Variations in disturbance distance lead to spatial variability in tunnel damage. These findings support tunnel design, reinforcement, and risk management in similar mines.
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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".