A numerical investigation on the influence of rockmass parameters and yield mechanics in pillar design
Bibliographic record
Abstract
Pillar stability guidelines have been presented by various authors in the past, based on a combination of empirical (mostly visual) observations from mining applications and simple numerical elastic stress indices or data from continuum plasticity analysis. There have been numerous developments in the past several decades in mechanistic classification (brittle spalling, effective continuum rockmass, structural control) and mechanism-appropriate modelling and design metrics. The design of pillar dimensions and spacing is a key factor in construction projects beyond mining including the engineering for deep geological repositories, hydroelectric power cavern complexes, underground storage, quarrying and other applications. These applications are situated in a variety of rockmass environments and at stress regimes from shallow to deep. In such applications, the geometry of the pillars and surrounding excavations deviates from the classic mine pillar scenario. This paper redefines design guidelines for pillar dimensioning based on numerical simulation results. It includes the consideration of rockmass deformation and yield (rockmass strength approach), brittle strength and damage behavior where appropriate. The investigation includes 3D FEM models with equivalent continuum networks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".