Reducing Uncertainty in Mining Applications Through Advanced Numerical Modeling
Bibliographic record
Abstract
ABSTRACT: As mining operations increasingly target complex geological environments—characterized by steeper open-pit slopes, deeper underground excavations, and challenging ore body access—traditional methods such as analytical solutions, design charts, and limit equilibrium analyses are often inadequate. These simplistic approaches struggle to capture the intricate geomechanical interactions inherent in modern mining, leading to significant uncertainties that can compromise safety, efficiency, and profitability. This invited talk explores how advanced numerical modelling provides a robust framework to address these challenges, offering enhanced reliability in design and operational planning. Through a series of practical case studies, this presentation will demonstrate the key role of advanced numerical methods in addressing complex mining scenarios. Key applications include: (1) evaluating the role of rock joint persistence in controlling cave propagation during block caving, (2) ensuring infrastructure stability in open stoping environments, (3) optimizing blast design and rock pre-conditioning for efficient stoping, and (4) assessing the combined influence of jointing and porewater pressure on slope performance and runout behavior. The talk will emphasize the pivotal role of selecting appropriate numerical toolsets to reduce these uncertainties. By leveraging advanced computational techniques, namely the finite-discrete element method, these case studies illustrate how tailored numerical approaches can improve predictions of rock mass behavior, enhance design reliability, and optimize operational strategies. The discussion will underscore the importance of integrating site-specific geological data with sophisticated modelling to achieve safer and more efficient mining operations.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Invited engineering talk on advanced numerical modelling to reduce uncertainty in mining design.
It discusses numerical modeling for mining applications, not methods or practices of research.
Mining geomechanics numerical modeling applications; domain engineering methods, not metaresearch.
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 | 0.005 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".