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Record W1964296383 · doi:10.1139/t07-066

Application of a multilaminate model to simulate the undrained response of structured clay to shield tunnelling

2008· article· en· W1964296383 on OpenAlexafffundvenueabout
Hoang K. Dang, Mohamed A. Meguid

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsGeotechnical engineeringQuantum tunnellingSettlement (finance)AnisotropyParametric statisticsFinite element methodGeologyShieldStructural engineeringEngineeringMaterials scienceMathematicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

A constitutive model based on the multilaminate framework has been implemented into a finite element program to investigate the effect of soil structure on the ground response to tunnelling. The model takes into account the elastic unloading–reloading, inherent and induced anisotropy, destructuration, and bonding effects. The model is successfully calibrated and used to investigate the undrained response of structured sensitive clay in the construction of the Gatineau tunnel in Gatineau, Quebec. Numerical results were compared to the field measurements taken during tunnel construction. To improve the performance of the numerical model, an implicit integration algorithm is implemented and proven to be very effective when coupled with the multilaminate framework as compared to the conventional explicit integration methods. The effect of different soil parameters including bonding and anisotropy on the tunnelling induced displacements and lining stresses is also examined using a comprehensive parametric study. The results indicated that soil bonding and anisotropy have significant effects on the shape of the settlement trough as well as the magnitudes of surface displacements and lining stresses induced by tunnelling.

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: none
Teacher disagreement score0.628
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations14
Published2008
Admission routes4
Has abstractyes

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