Reassessing Janssen's equation for cave stress estimation in block cave mining
Bibliographic record
Abstract
Cave mines operating at greater depths are faced with higher draw column heights, typically ranging from several hundred metres to over one kilometre. This results in significantly higher cave stress on the production level, making reliable stress estimation critical for assessing long-term stability. The current approach to estimating cave stresses in the draw column relies on Janssen's equation, developed initially using the Method of Differential Slices (MDS) and based on laboratory observations of corn and bulk solids in storage silos. This article first reviews the MDS and the assumptions used to derive Janssen's equation. The article also discusses how the limitations of the analogues (e.g. corn material and full-to-empty silo condition) used to develop the equation do not represent conditions equivalent to those observed in cave mining (e.g. fragmented rock blocks undergoing fragmentation and no-column to full-column condition as cave propagates). These mechanistic differences may lead to erroneous estimations of cave stresses. While Janssen's equation is a valuable initial tool for estimating cave stresses, practitioners must carefully assess its assumptions and limitations before applying it to engineering designs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".