Calculating Bias-Free Volumetric Fracture Counts (VFCs) in Underground Works and Their Use in Estimating Rock Mass Strength and Deformability Parameters
Bibliographic record
Abstract
This paper initially provides a practical example on how to estimate a bias-free volumetric fracture count (VFC—fractures/m3) in a tunnel and incorporate it into a new and unified volumetric-based Geological Strength Index (V-GSI) chart. The quantified V-GSI chart and the methods shown in the practical example were used extensively as tools to assess rock mass conditions and assist in support determinations on the WestConnex M8 Motorway tunnel project in Sydney, Australia. The reliability of the strength and deformability estimates obtained using the V-GSI ratings while tunneling within the Hawkesbury Sandstone is demonstrated here by providing an example of deformation results obtained through 3-D finite element analysis in a single location in the tunnel. The modelling results are compared to measure convergence in the tunnel in this location, which demonstrated good correlation between predicted and observed deformation. This provides validation that the V-GSI chart and associated Hoek–Brown strength and deformability equations can be used with some confidence to determine potential deformation in underground works.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".