Integrating photogrammetry and discrete fracture network modelling for improved conditional simulation of underground wedge stability
Why this work is in the frame
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Bibliographic record
Abstract
Over the last decade, the advantages of discrete fracture network (DFN) models over more conventional tools for key block stability analysis have become increasingly apparent. Without their need for a series of simplifying assumptions regarding the fracture system, rock wedge formation and excavation geometry, DFN’s ability to accurately capture the underground rock mass is clear. Coupled with the probabilistic consideration of block formation and joint strength parameters, they provide a valuable tool to the engineer for risk-based underground stability assessments. However, recent changes in DFN technology have allowed a step change in modelling realism to be incorporated. A major improvement is the ability to generate DFN models directly conditioned to photogrammetric surveys so that the kinematic assessment is carried out on a structural description that accurately reflects the scanned location. This conditioned DFN model is embedded within an unconditioned stochastic description of the rock mass away from the scanned rock mass exposure, thus, providing a model that is constrained by the available geotechnical data (boreholes, scanning, trace mapping) but accurately conditioned to the key observed structures. The result is an ability to optimise excavation and ground support designs with a method that intelligently handles the natural heterogeneity imposed by the rock mass, combining what we see with what we know.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it