Probabilistic pit slope stability analysis targeting a reliability-based design acceptance criteria: a parametric study
Bibliographic record
Abstract
Design of open pit slopes is a decision-making process which aims to maximise the ore recovery while minimising the excavation volumes. The current practice of designing open pit slopes adopts the widely accepted Guidelines for Open Pit Slope Design by Read & Stacey (2009). An optimum design should satisfy a design acceptance criteria (DAC). However, designing open pit slopes is a complex process that involves inherent risks and uncertainties. As a result, reliability analyses are becoming increasingly important for performance-based slope designs. In a reliability approach, the amount of information on the slope materials and behaviour would reflect the reliability of a slope design. This paper presents a parametric study defined by the uncertainties of the rock mass strength properties and the slope geometric configurations at three different design reliability levels targeting a reliability-based DAC (RBDAC). The reliability assessment is performed using probabilistic analysis adopting the two-dimensional limit equilibrium method and Monte Carlo simulations. The input variables for the rock mass strength are defined through probability density functions (PDF) that capture the natural variability, while the input variables of geological structures are defined through kinematic assessments. The PDFs of the rock mass strength properties were modelled based on the generalised Hoek–Brown criterion using the mean, coefficient of variation (COV) and dependence between quantitative properties of the criterion. Results show that most of the resultant pairs of Factor of Safety (FoS) and probability of failure and associated COV of the resulting FoS (COVFoS) are consistent with the RBDAC. Based on this, a redesign is proposed showing the applicability of the RBDAC and comparing it to the current DAC. This approach has significant implications for slope optimisation or mitigation plans for future pushbacks in case of instabilities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".