A review of structural data collection methodologies for discrete fracture network generation
Bibliographic record
Abstract
The variability in a rock mass must be considered in geotechnical engineering analyses and designs. Discrete fracture network (DFN) modelling accounts for structural variability in a rock mass, providing a valuable tool that may be used in various geotechnical applications. DFNs provide a statistical representation of the rock mass discontinuity system by the stochastic generation of discontinuity sets. This is based on structural data collected in the field from boreholes or by mapping exposures. DFN generation therefore involves structural data collection from which discontinuity sets may be defined. Each discontinuity set within a single structural domain is characterised using statistical distributions to describe the orientation, spacing, and trace lengths of the discontinuities, which are used to provide input parameters for DFN generation. The quality of a DFN therefore relies on the quality of the field data and its interpretation. This paper reviews the various approaches available to collect structural data for DFN generation. The advantages and limitations of each method is given, and data collection and analysis strategies are outlined.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".