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Record W2419620538 · doi:10.1139/cgj-2015-0467

Constitutive model for rate-independent behavior of saturated frozen soils

2016· article· en· W2419620538 on OpenAlexvenueno aff
Seyed Ali Ghoreishian Amiri, Gustav Grimstad, Mahdi Kadivar, Steinar Nordal

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringSoil waterSuctionConstitutive equationMechanicsStress (linguistics)Coupling (piping)Overburden pressureMaterials scienceEffective stressGeologyThermodynamicsSoil scienceComposite materialFinite element methodPhysics

Abstract

fetched live from OpenAlex

The mechanical behavior of frozen soils is strongly affected by the amount of ice. The amount of ice depends on the temperature and the applied mechanical stresses. The influence of ice content and temperature on the mechanical behavior and the coupling effects on the reverse direction can be mentioned as the main difference between frozen and unfrozen soils. In the light of this difference, an elastoplastic constitutive model for describing the stress–strain behavior of saturated frozen soils is proposed. By dividing the total stress into fluid pressure and solid phase stress, in addition to consideration of the cryogenic suction, the model is formulated within the framework of two-stress state variables. The proposed model is able to represent many of the fundamental features of the behavior of frozen soils, such as ice segregation phenomenon and strength weakening due to pressure melting. In the unfrozen state the model becomes a conventional critical state model. Typical predictions of the model for simulating the characteristic trends of the frozen soil behavior is described qualitatively. Model predictions are also compared with the available test results and reasonable agreement is achieved.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.998

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)0.0030.000

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 head0.047
GPT teacher head0.253
Teacher spread0.206 · 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.

Study designObservational
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

Citations116
Published2016
Admission routes1
Has abstractyes

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