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Modeling and Implementation of the Isotache Concept for Long-Term Consolidation Behavior

2012· article· en· W2074390730 on OpenAlexaff
Yoichi Watabe, Serge Leroueil

Bibliographic record

VenueInternational Journal of Geomechanics · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConsolidation (business)Oedometer testStrain rateCreepVoid ratioGeotechnical engineeringPower functionMathematicsMaterials scienceGeologyMathematical analysisComposite materialAccountingSoil science

Abstract

fetched live from OpenAlex

A practical use of the isotache concept in creep settlement prediction is newly introduced in this study. The isotache concept introduces a unique relationship between the strain and the consolidation pressure corresponding to the strain rate in association with viscosity. The authors have proposed a method to simply introduce the isotache concept, which uses a compression curve normalized by preconsolidation pressure [ε–log(σ′/σp′)] and a strain rate dependency relationship between the preconsolidation pressure (σp′) and strain rate (ε˙=dε/dt). The strain rate dependency relationship is modeled as an equation based on power law. According to the authors’ method, the slope of the strain rate dependency relationship (log⁡σp′–logε˙), which coincides with Cαe/Cc, can be calculated as a function of strain rate, showing that Cαe/Cc is not constant but decreases with decreasing strain rate. In engineering practice, consolidation settlement is generally estimated based on the compression curve obtained from the 24-h incremental loading oedometer test, which corresponds to a strain rate of about 1×10−7 s−1. Using the compression index Cc at the consolidation pressure and initial void ratio e0, creep settlement can be easily calculated as a function of Cc/(1+e0) and in situ strain rate (several orders of magnitude smaller than 1×10−7 s−1).

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 score0.396
Threshold uncertainty score0.185

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 head0.282
Teacher spread0.268 · 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

Citations37
Published2012
Admission routes1
Has abstractyes

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