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Record W2919081846 · doi:10.1139/cgj-2018-0583

Thermal effects on mechanical behaviour of soil–structure interface

2019· article· en· W2919081846 on OpenAlexvenueno aff
Soheib Maghsoodi, Olivier Cuisinier, Farimah Masrouri

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsCohesion (chemistry)Geotechnical engineeringSoil structureDirect shear testStiffnessSoil waterClay soilShear (geology)Shear strength (soil)Materials scienceThermalGeologyComposite materialSoil scienceChemistry

Abstract

fetched live from OpenAlex

Mechanical behaviour of the soil–structure interface plays a major role in the shear characteristics and bearing capacity of foundations. In thermoactive structures, due to nonisothermal conditions, the interface behaviour becomes more complex. The objective of this study is to investigate the effects of temperature variations on the mechanical behaviour of soils and the soil–structure interface. Constant normal load (CNL) and constant normal stiffness (CNS) tests were performed on the soil and soil–structure interface in a direct shear device at temperatures of 5, 22, and 60 °C. Fontainebleau sand and kaolin clay were used as proxies for sandy and clayey soils. The sandy soil was prepared in a dense state and the clayey soil was prepared in a normally consolidated state. Results show that the applied thermal variations have a negligible effect on the shear strength of the sand and sand–structure interface under CNL and CNS conditions, and the soil and soil–structure interface behaviour could be considered thermally independent. In clay samples, an increase in the temperature increased the cohesion and consequently the shear strength, due to thermal contraction during heating. The temperature rise had less impact on the shear strength in the case of the clay–structure interface than in the clay samples. The adhesion of the clay–structure interface is less than the cohesion of the clay samples.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.585

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 integrity0.0000.001
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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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 designBench or experimental
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

Citations113
Published2019
Admission routes1
Has abstractyes

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