Implications of collecting additional data for slope design in an open pit operation
Bibliographic record
Abstract
Geotechnical stability analysis and design in open pit mines requires access to representative geological, structural, hydrogeological, and rock mass models. The quality and quantity of collected geomechanical data used to build such models can have significant implications in the design of safe and economically viable slopes. Data collection is a continuous process throughout the life of a mine. The process starts from exploration to conceptual/preliminary feasibility, feasibility, design and construction, operations (early production/late production), and mine closure. As geomechanical data is often limited at an early stage of a project, the estimated variability of the geotechnical factors may be larger than the actual data variability. This can potentially result in a conservative selection of the pit slope angle. Collecting additional geomechanical data at later stages of the mine development may contribute to reducing the uncertainty associated with the geotechnical properties and a steeper slope angle may be used for the design. This can result in significant economic benefits. This paper investigates the impact of access to new data over time, following a series of geomechanical data collection campaigns, on design decisions. This is illustrated by comparing the resulting slope design, at three stages of the mine project, at a South African open pit mine. To demonstrate this, a series of slope stability analyses were performed using limit equilibrium tools. For each project stage, the slope angle design was modified until a similar probability of failure of 5% or less was obtained. The corresponding factor of safety distributions for each project stage were then compared to evaluate the impact of collecting additional data on the resulting slope design. The results showed that data variability can differ significantly from one project stage to another. Data variability may be greater at more advanced project stages (i.e. a wider distribution of the factor of safety). Furthermore, the results showed that, even for the cases with greater variability, steeper slope angles were possible at more advanced stages for the proposed design requirement (probability of failure < 5%). In this investigation, as more data became available, the uncertainty associated with data variability could be sufficiently reduced to allow the selection of less conservative slope angles. This can be used as a managerial tool in developing data collection strategies and allocating the necessary funds, and can lead to important economic benefits to the mining operation.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".