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Record W4290102376 · doi:10.1139/cgj-2022-0172

Thermomechanical response of kaolin clay–concrete interface in the context of energy geostructures

2022· article· en· W4290102376 on OpenAlexvenueno aff
Amirhossein Hashemi, Melis Sütman, Hossam Abuel-Naga

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceGeotechnical engineeringDirect shear testShear (geology)Composite materialSurface finishShear stressShear strength (soil)Soil waterGeology

Abstract

fetched live from OpenAlex

The analysis and design of energy geostructures are mainly characterised by the mechanical behaviour of the soil–structure interface in non-isothermal conditions. In this study, direct shear tests are conducted to investigate the shear behaviour of soil and soil–structure interface in the practical temperature range of energy geostructures (i.e., 8–45 °C). The interface in this study is formed of kaolin in contact with concrete specimens with different roughness. Tests are performed on normally consolidated and overconsolidated interfaces following the unloading/reloading paths to better understand the impact of thermal strain on the interface behaviour. The volumetric response of the interface is observed to be highly influenced by the thermal strains experienced during heating/cooling. The soil stress level and the most recent soil stress history are identified as the primary determinants of thermally induced changes in interface shear strength. For normally consolidated interfaces, the temperature increase led to higher adhesion and slightly lower friction angle, whereas higher adhesion and identical friction angles were found for tests conducted on cooled specimens. Temperature does not seem to affect the shear strength of overconsolidated interfaces. Finally, a conceptual understanding of the temperature effect on interface shear behaviour is provided by analysing data from the literature.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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

Citations32
Published2022
Admission routes1
Has abstractyes

Explore more

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