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Record W4210759914 · doi:10.1139/cgj-2021-0373

Mechanisms of shear strain accumulation in laboratory experiments on sands exhibiting cyclic mobility behavior

2022· article· en· W4210759914 on OpenAlexvenueno aff
Francisco Humire, Katerina Ziotopoulou

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsShear (geology)Geotechnical engineeringMaterials scienceShear stressSimple shearRelative densityOverburdenTriaxial shear testShearing (physics)Cyclic stressStrain rateDirect shear testComposite materialGeologyMicrostructure

Abstract

fetched live from OpenAlex

The factors and mechanisms controlling the accumulation of shear strains of clean uniform sands exhibiting cyclic mobility behavior under level-ground conditions are examined. This phenomenon is investigated through a series of constant-volume cyclic direct simple shear (DSS) tests subjected to uniform and irregular loading conditions, and undrained cyclic element tests collected from the literature. Experimental data show that the rate of shear strain accumulation per loading cycle depends on the relative density, cyclic stress amplitude, and effective overburden stress. Mechanisms of shear strain accumulation are investigated by decoupling the shear strain developed in each loading cycle in two components: γ 0 , developed at near-zero effective stress, and γ d , developed during dilation. Results show that γ 0 mostly depends on the shear strain history, while γ d depends on the cyclic stress amplitude and relative density. These dependencies of γ d and γ 0 are used to provide an explanation for the gradual decrease of the rate of shear strain accumulation that is observed while increasing the number of post-triggering loading cycles in tests performed on dense specimens.

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 score0.265
Threshold uncertainty score0.765

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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