Residual strength of underground structures in service
Bibliographic record
Abstract
Old tunnels suffer from deterioration and it is necessary to assess their residual strength to properly organize their repair and strengthen them. The stress state of underground structures evolves over time, firstly because of the advancement of the working face during construction, then due to gradual changes in soil properties and mechanical properties of materials composing the infrastructures, such as reduction of cohesion, loss of strength and (or) stiffness, etc. These changes are caused by combined actions such as creep and (or) weathering processes as well as the appearance of cracks that induce redistribution of stresses and therefore strains. This study investigates tunnels and galleries of the Paris underground. Macromodeling based on the finite element method allows consideration of different scenarios of tunnel-lining deterioration, specifically at the extrados of the galleries or tunnels. To understand changes in the stress state, and also damage occurrence and associated redistributions (stress and strain), it is necessary to model the delayed deformations. To model the infrastructure behavior, a time-dependency approach has been chosen. This approach can quantify the damage and predict the residual strength of this type of underground structure. An elastic–viscoplastic constitutive model with strain-softening is used to reproduce the appearance of the degraded zones and their behavior. The results obtained with the numerical approach reproduce Paris metro tunnel behavior, corroborate geoendoscopy results, clarify their interpretation, and improve the management of infrastructure repairs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".