Numerical analysis by comparing the Drucker-Prager and Mohr-Coulomb behaviors to optimize the best sites for constructing a new tunnel adjacent to the old one
Bibliographic record
Abstract
The tunnels are one of the most significant infrastructures for facilitating mobility in mountainous terrain. It has several benefits, including shortening distances, time, and ease of transportation. To make it easier, developed countries create several tunnels close to each other. Engineers face the necessity of building these new tunnels near existing ones; this infrastructure must be safe. In this paper, for this numerical study, we used the OptumG2 software to analyze the position of a new tunnel created in six locations: bottom, top, left (two places at 12 m and 32 m), and right (two places at 12 m and 32 m) by the finite element method. This study was analyzed using two criteria: the equivalent of Drucker Prager and Mohr Coulomb. We are interested in the total field of the displacement, the vertical displacement of the model, the displacement, bending moment, shear, and normal force of the concrete lining. The findings of this numerical analysis using the Drucker Prager and Mohr Coulomb behaviors to determine the ideal locations for building a new tunnel next to an existing one show that the new bottom tunnel might have a significant influence on the stability of the existing tunnel. It was also observed that the maximum displacement present when we add a new tunnel at the bottom meets the Drucker-Prager criteria.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".