Use of Stochastic DFN-DEM Modelling for Overall and Inter-Ramp Slope Stability Analysis
Bibliographic record
Abstract
Abstract Geometric Discrete Fracture Network (DFN) models are constructed in this research project, where limited and insufficient field structural data were exploited statistically in terms of observed parameter distribution at the mine-scale perspective. The construction of a DFN model employing a disaggregate technique was suggested by descriptive statistics of intensities (P10), orientation and length. The DFN model was validated by field data. The calibrated DFN model was then exported in a two-dimensional Distinct Element Model (DEM) to assess the stability of a critical pit wall where the multiple block failures were recorded at the bench and inter-ramp slope scale. The DFN-DEM approach adopted in this study could model the slope stability state using the shear strength reduction (SSR) method. According to the results of the simulations, the critical strength reduction factor (SRF) values derived from each numerical simulation were highly dependent on the DFN geometry configuration, and the trace length. To quantify the uncertainty associated with the assumptions established during the DFN-DEM modelling process, a series of models were simulated with different DFN configuration. At the inter-ramp slope scale, the simulations were predominantly unstable (SRF≤1.0) over 30 DFN realisations. The probability of failure (PoF) averaged 70% with a mean safety factor of 0.93 for the simulated DEM slope models while using the LE method, an average PoF of 40% and mean safety factor of 1.08 were estimated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".