A disturbance index-based approach for determining the effective numerical model size for tunnels
Bibliographic record
Abstract
In numerical modelling of tunnelling, the simplified model sizes, typically approximated as six times the tunnel radius, often overlook rock mass quality and underestimate tunnel excavation disturbances, especially for deep tunnels. In this study, we propose a novel approach to determine the effective size of a numerical tunnel model by utilising a rock disturbance index, accounting for poor rock mass quality. Formulations of the rock disturbance index were developed based on the Mohr-Coulomb and Hoek-Brown criteria and were used to determine appropriate tunnel model sizes. Numerical simulation results reveal that the conventional approach can lead to distorted results. Through the developed solutions, the novel approach determines the effective numerical model sizes for different rock mass qualities by setting the rock disturbance index at 2.5% as the model boundary condition. The errors associated with this novel approach do not exceed 1%. Finally, a sensitivity analysis of the effective numerical model size was conducted, indicating that the geological strength index and uniaxial compressive strength have negative influences, whereas initial far-field stress has a positive effect. The novel approach can be applied to elastic-plastic problems in tunnel rock simulations, with the developed Hoek-Brown solution being more conservative than the Mohr-Coulomb solution.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".