Enhancing tunnel stability in the Himalayas: Empirical design support through numerical modelling
Bibliographic record
Abstract
Abstract This study focuses on assessing the effectiveness of an empirically recommended support design through numerical modelling. Numerical techniques are utilized to accurately evaluate tunnel stability under various geological conditions. Numerical analysis is conducted on two different rock types along the tunnel route, employing the recommended support design based on the existing rock mass rating ( RMR ) and Q ‐system. Furthermore, support recommendations are made using the modified RMR and Q ‐system, considering the impact of stress. The numerical analysis indicates that the support recommended by the existing RMR may not substantially impact the total displacement and vertical stresses around the tunnel crown in the “Fair” to “Poor” rock mass of the study area. However, extending the bolt length by modifying the RMR to incorporate stress effects results in a reduction of total displacement and vertical stresses, ultimately achieving a stable level. These results underline the importance of considering stress effects and utilizing modified support designs to enhance tunnel stability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".