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Record W2107721400 · doi:10.1139/cgj-2013-0265

Description of compression behaviour of structured soils and its application

2014· article· en· W2107721400 on OpenAlexvenueno aff
Chao Yang, John Carter, Daichao Sheng

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsGeotechnical engineeringSoil waterCompressibilityLogarithmCompression (physics)Effective stressSoil mechanicsLogarithmic scaleStiffnessSensitivity (control systems)Nonlinear systemMathematicsGeologyMaterials scienceMechanicsSoil scienceEngineeringMathematical analysisComposite material

Abstract

fetched live from OpenAlex

One of the most distinct characteristics of structured soils is the nonlinearity in the normal compression lines in a plot of specific volume or voids ratio against logarithmic mean or vertical effective stresses, when compared with reconstituted soils. The change in the compressibility (or compression index) with loading is attributed to structure degradation and is expressed as a function of the plastic straining. A direct description of the compression behaviour of structured soil is then established. The validity of this approach is examined via merely incorporating the newly defined normal compression line into the modified Cam-Clay constitutive model. Comparisons against a series of experiments on different types of soils illustrate the feasibility and advantage of the adopted methodology. The dependence of shear strength on the compression behaviour considered initially in critical-state soil mechanics is reemphasized here for structured soils. Analysis also indicates that the stiffness sensitivity coefficient, S λ , should be considered together with the traditional strength (or stress) sensitivity coefficient, S t (or S σ ), to better characterize the sensitivity of structured soils.

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 score0.237
Threshold uncertainty score0.411

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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