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Record W2999112066 · doi:10.1061/9780784481479.010

Cyclic Shearing Response of Granular Material in the Semi-Fluidized Regime

2018· article· en· W2999112066 on OpenAlexaff
Andrés R. Barrero, W. F. Oquendo, Mahdi Taiebat, Arcesio Lizcano

Bibliographic record

VenueGeotechnical Earthquake Engineering and Soil Dynamics V · 2018
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShearing (physics)Materials scienceComposite materialGranular material

Abstract

fetched live from OpenAlex

Liquefaction is a phenomenon usually observed in saturated granular soils when subjected to monotonic or cyclic shearing under constrained volumetric conditions. Liquefaction is usually associated with a loss of mean effective stress due to generation of excess pore pressure during the shearing of soil. In this process the particles reduce, or in the extreme case may lose, contact with each other. This nearly contact-free state is known as “semi-fluidized regime”. In this state, the soil behaves almost like a viscous fluid, but the properties and origins of this response are not well understood. This study presents a series of numerical simulations using discrete element method (DEM) to model the constant volume cyclic shearing of three granular assemblies with different packing fractions. The variations of the stress-strain response and the corresponding coordination number for each sample are tracked when the semi-fluidized regime is achieved. The results show the semi-fluidized regime in all of these assemblies regardless their packing fraction. This micro-mechanics based approach to the analysis can elaborate on the variations of the coordination number, mean effective stress, and developed shear strains before and after entering such regime.

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 score0.781
Threshold uncertainty score0.851

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 science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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