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Record W2231914255 · doi:10.1139/cgj-2014-0554

Residual strength of underground structures in service

2016· article· en· W2231914255 on OpenAlexvenueno aff
Taous Kamel, Ali Limam, Claire Silvani

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsCreepCohesion (chemistry)StiffnessViscoplasticityGeotechnical engineeringResidual strengthStructural engineeringResidualFinite element methodResidual stressConstitutive equationConsolidation (business)GeologyEngineeringMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.207
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2016
Admission routes1
Has abstractyes

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