The importance of geological and material model detail in modelling progressive failure: Andes deep open pit
Bibliographic record
Abstract
This study investigates the effects of geological modelling detail and material model complexity on the numerical modelling of progressive failure in deep open pit mining operations. The inter-ramp failure case is a common failure mechanism in open pit mine slopes. The research aims to identify the individual and combined effects of these factors on the precision of predicting progressive failure behaviour by systematically varying the level of detail included in the geological model and the complexity of the selected material model. The investigation aims to deepen our understanding of material model complexity and geological model detail in capturing progressive failure mechanisms. The case study demonstrates how a thorough geological model and efficient back-analysis techniques can successfully replicate observed progressive failure mechanisms, providing valuable information for infrastructure and mining industries. The findings will offer practitioners advice on the appropriate level of complexity needed for different levels of a numerical simulation study of progressive failure. This research contributes to a better understanding of progressive failure in deep open pit mining slopes by examining the combined effects of geological model detail and material model complexity. It improves numerical modelling techniques, ultimately aiding open pit mining operations in making better safety and decision-making choices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".