Probabilistic Step-Path Slope Stability Analysis Using Spatially Constrained DFN Models – A Case Study at Tasiast Mine
Bibliographic record
Abstract
Abstract In this paper, geotechnical and structural data from an open pit mine were used to generate spatially constrained largescale DFN models. Data from geotechnical boreholes were used to develop 3D block models of volumetric fracture intensity (P32), using a geostatistical simulation method. The block models were used for spatially constraining 3D geo-cellular DFN models. The fracture centers within the DFN cellular grid are controlled by the cellular P32 values, estimated using the 3D block model. The joints orientation was bootstrapped from drillhole loggings whereas trace length data, collected across the pit area, were used to further calibrate the geo-cellular models. The resulting DFN models represented the spatial variation of fracture geometry and intensity along the pit area and were employed for numerical modeling of step-path slope stability analysis of the pit slopes. 2D cross sections of the 3D DFN models were embedded into 2D finite element models of the pit slope for a probabilistic step path stability analysis. In addition, the DFN models were used to estimate the rock bridge percentage along the pit walls which was ultimately used to assess the composite rock mass strength for a limit equilibrium analysis of the overall pit slope. The results of the two numerical approaches were compared to a simplistic approach assuming an average rock mass structural condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".