Modélisation numérique de l'apport du renforcement par boulonnage du front de taille des tunnels
Bibliographic record
Abstract
We have first developed empirically the technique of reinforcement of the cutting face of tunnels, which allows for the excavation of full sections in layers of hardened soils or soft rock. Although numerous studies of numerical simulations have been proposed so far, the three-dimensional (3D) simulation of reinforcement of the cutting face of tunnels remains a heavy and costly procedure in terms of preparation and calculation, due in particular to the geometrical complexity and the different scale levels involved. Hence, it appears necessary to have at our disposal, especially at the pilot study stage, some simplified models that allow for a quick evaluation of the efficiency of the reinforcement. The bibliographical survey of the actual practices has led us to consider two methods: the first one consists of considering the effect of inclusions as equivalent to a pressure applied to the front, and the second one consists of a reinforcement of the cohesion ahead of the front along the length of the reinforcement. These two approaches have been compared to 3D calculations to evaluate their relevance.Key words: soil-structure interaction, tunnels, deformation, reinforced soils, numerical modelling, analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".