Extrusion analysis of a bolt-reinforced tunnel face with finite ground-bolt bond strength
Bibliographic record
Abstract
In this paper a new analytical model is proposed to analyse the displacement behaviour of a tunnel face reinforced by bolts, using the homogenization approach for periodic media. Based on previous works, this new model was developed to take into account a finite bond strength of the groutsoil interface, which is more realistic than the perfect bonding assumption. The interface behaviour is simulated by an elastic perfectly plastic constitutive law. The interface sliding occurs when the shear stress reaches the bond strength. This new feature allows for a more correct estimate of the bolt tension and the quantitative contribution of the bolt to reducing displacement of the ground surface. Despite using less restrictive hypotheses, the solution process remains sufficiently simple, thus preserving the analytical character of the solution. This new analytical model has been validated by three-dimensional (3D) numerical calculations using the finite difference code FLAC3D and by comparing its predictions with in situ data from the Tartaiguille Tunnel construction project, which forms part of the Mediterranean TGV (high speed railway) network. The developed approach results in simple and efficient design tools, which are very useful at the preliminary design stage of a project.Key words: tunnels, soilstructure interaction, reinforcement, numerical modelling and analysis, deformation, plasticity.
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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 | 0.001 |
| 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 | 0.001 | 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".