Development of thin, spray-on liner and composite superliner area supports for damage mitigation in blast- and rockburst-induced rock failure events
Bibliographic record
Abstract
Research has been performed for the Workplace Safety and Insurance Board of Ontario (WSIB) to characterize the capabilities of innovative mining support agents, designated as spray-on lining materials (TSL's), as well as combinations of TSL's and conventional spray supports, for mitigating dynamic failure effects created by simulated rockbursts.The assessment of support capabilities of TSLs and ultra-thin hybrid liner supports (superliners) is novel and constitutes work that is unique in the field of underground excavation support design.This study has been performed to assess the capabilities of TSL and conventional spray-on support systems for preventing rock and support material damage that often results due to rockbursting.In this research, TSL products and superliner combinations of each with ultra-thin shotcrete or fibrecrete layers (at 5 and 3 cm thicknesses, respectively) have been tested.Support performance was studied using field scale explosive detonation trials to simulate dynamic failure effects that are known to develop during typical rockburst events.Multiple seismic and high speed photographic monitoring techniques were used to provide detailed information concerning rock motion, surface fracturing, ejected fragment motion and support liner survivability characteristics.The results of this study have validated that thin, spray-on linings (TSL's) and variant layer combinations may be as effective as or better than conventional support materials for mitigating rockburst or like damage in highly stressed mine environments.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".