Design of an open-pit gold mine by optimal pitwall profiles
Bibliographic record
Abstract
The overall steepness of pitwalls significantly influences the financial return of an open pit mine. In current practice, pitwall profiles are planar in cross-section. In this paper, a new geotechnical software, OptimalSlope, is employed to determine depth-varying optimal pitwall profiles for each slope sector of the mine. OptimalSlope solves a mathematical optimization problem where the overall steepness of the pitwall is maximized for the assigned stratigraphy, rock properties, and Factor of Safety (FoS). Bench geometries (bench height, bench-face inclination, and minimum berm width) are incorporated into the optimization as constraints that bind the maximum local inclination of the sought optimal profile together with any other constraint related to any geological discontinuities that may influence slope failure. The optimal profiles are always steeper than their planar counterparts—that is, the profile exhibiting the same FoS, generally up to 8 degrees, depending on rock type and constraints. To showcase the financial gains that can be achieved via OptimalSlope, the design of a gold mine in a complex geology dominated by weak rocks was initially carried out for planar pitwalls and then for optimal pitwall profiles. The pit has been divided into five geotechnical sectors, each requiring a different pitwall profile design. Adopting optimal slope profiles led to a 52.7% higher net present value and reductions in the carbon footprint and energy consumption of 0.0613 million tonnes CO2 eq and 31.3 million MJ, respectively, due to a 3.5% reduction of rock waste volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".