Delineation of hazard-based design events for dynamic support system analysis
Bibliographic record
Abstract
The objective of a dynamic ground support system is to manage excavation damage associated with rockburst events. To achieve this objective, a dynamic support system must be engineered to withstand the unique loading conditions imparted in a high-stress, burst-prone environment. The process of defining those unique loading conditions focuses on identifying the expected failure mechanisms and estimating the expected damage intensity and failure severity to quantify the load demand that may be imparted onto the support system. Identification of the maximum probable ‘design event’ is a critical input parameter for support demand assessments in dynamic ground support system design. Defining dynamic loading conditions unique to probable ‘design events’ requires detailed seismic data analyses. Using a case study from a deep Canadian mine, seismic data analyses are engaged to identify impacting seismic response parameters, which are then applied to seismic domain delineation. Frequency–magnitude trends (and associated b-value analyses) are then evaluated in domained areas to define the ‘design events’. Categorisation of the seismic hazard classes relies on the explicit ‘design events’ which deliver support demand input values necessary for optimising dynamic ground support design. Site-specific seismic data analyses deliver optimum load demand parameters appropriate for achieving an engineered dynamic ground support system design, effectively managing seismic hazard in a safe and economical manner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".