Prediction of Ground Conditions Ahead of an Advancing Tunnel Face by Quantification of Vector Orientation
Bibliographic record
Abstract
Observation and Measurement of displacements at the crown of the tunnel is an integral part of New Austrian Tunneling Method (NATM) in complex rock masses to verify the construction parameters, support systems and safety requirements. However, the prediction of ground conditions ahead of an advancing tunnel face is also an application, often less explored in the field by site engineers. Literature suggests displacement vector orientation is a good indicator of weak ground or fault/shear zones ahead of the tunnel face. There has been no quantification between the vector orientation and the properties of the weaker ground ahead. An attempt is made to quantify the effect of the properties of the weaker ground on the vector orientation numerically. The model results are initially compared with analytical solutions, followed by benchmarking with the results of other researchers. The factors which affect the vector orientation are the diameter of the tunnel, stiffness ratio of the rocks and in situ stress ratio. The effect of each of these parameters is studied independently. A correlation between the vector orientation and the variation in ground conditions is then established to predict the properties of the weaker ground ahead of the advancing tunnel face.
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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 | 0.000 | 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) | 0.001 | 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".