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Strain-Dependent Creep Behavior of Athabasca Oil Sand in Triaxial Compression

2016· article· en· W2310898398 on OpenAlexafffund
Zhechao Wang, R.C.K. Wong

Bibliographic record

VenueInternational Journal of Geomechanics · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsCreepGeotechnical engineeringMaterials scienceSofteningOil sandsHardening (computing)Strain hardening exponentGeologyComposite material

Abstract

fetched live from OpenAlex

Oil sand is a dense granular material with interlocking fabric. As a strain-softening material, oil sand exhibits a more complex creep behavior than that of strain-hardening geomaterials. The creep behavior of oil sand could be excessive and detrimental to surface and subsurface facilities in the long term. This paper describes a study on creep behavior of oil sands. A series of triaxial compression creep tests were performed on oil sand to investigate its creep behavior at different stress and strain levels. The creep behavior of oil sand is dependent not only on both time and stress but also on initial inelastic strain, and it is influenced by the growth of shear bands. A strain-dependent creep model was proposed to describe the prepeak creep behavior of the oil sand. With this model, the dependence of creep rate of oil sands on time, stress, and strain is taken into consideration properly. It was argued that the change in the microstructure of oil sand could be uniquely represented by the inelastic strain of oil sand. Therefore, the potential for plastic flow could be used in modeling oil sand creep. Finally, the general creep rate–stress–inelastic strain relations of strain-hardening and strain-softening soils were developed according to the critical state theory of soil and the inelastic strain-dependent creep rate of 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2016
Admission routes2
Has abstractyes

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