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Record W4386254307 · doi:10.1680/jgeot.22.00278

Shear resistance relaxation of granular materials under vibration and restricted deforming conditions

2023· article· en· W4386254307 on OpenAlexaff
Tao Xie, Peijun Guo, Dieter Stolle

Bibliographic record

VenueGéotechnique · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsShearing (physics)VibrationMaterials scienceShear (geology)Granular materialMechanicsDeformation (meteorology)Composite materialStructural engineeringGeotechnical engineeringGeologyPhysicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

The phenomenon of vibration-induced shear resistance relaxation (ViSRR) refers to the loss of shear resistance in granular materials subjected to vibration, without an observable increase of excess pore pressure, under restricted deforming conditions. Essentially, ViSRR occurs when the total deformation of granular materials is unable to keep up with the plastic deformation induced by the vibration, given the restricted deforming conditions. In this study, ViSRR is investigated through a series of specially designed experiments together with theoretical simulation based on the extended shear transformation zone (STZ) model developed for granular materials. The results demonstrate that ViSRR takes place in granular materials under monotonic shearing when the vibration is superimposed, and the magnitude of shear resistance relaxation depends on the intensity of the vibration. The occurrence of ViSRR is subject to two fundamental prerequisites: superimposed vibration as the driving force for plastic deformation, and restricted total deformation that impedes a granular material's deformability to follow the development of plastic deformation. The simulation results obtained from the extended STZ model are in agreement with laboratory test results, indicating that the model is effective in describing the process of shear resistance relaxation and the accompanying volumetric strain during vibration.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.394

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 integrity0.0000.000
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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