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Record W2995351048 · doi:10.1680/jgeen.19.00162

Measurement and correlations of <i>K</i> and <i>V</i>s anisotropy of granular soils

2019· article· en· W2995351048 on OpenAlexaboutno aff
Xin Kang, Zhao Xia, Renpeng Chen

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsAnisotropySoil waterGranular materialMaterials scienceParticle (ecology)Stress (linguistics)Particle sizeGeotechnical engineeringComposite materialMineralogyGeologyOpticsPhysicsSoil science

Abstract

fetched live from OpenAlex

The coefficient of lateral earth pressure at rest (K0) and stress-dependent shear wave velocity (Vs) of granular soils under one-dimensional compression loading were measured using a modified floating wall consolidometer-type bender element testing system. The Vs of the granular soils was monitored through paired bender elements installed in the floating wall and on top and base platens, and the Vs anisotropy of the soils was investigated by comparing the Vs values in three orthogonal directions. Granular materials such as glass beads (GB) and Ottawa sand with different shapes and sizes were adopted and the effects of particle shape and size and stress level on K0 and Vs anisotropy were discussed. From the laboratory observations, K0 was found to be dependent on the particle shape and surface roughness. The anisotropic stress state was found to play a major role in governing the Vs anisotropy of the GB; however, the Vs anisotropy of the sand resulted from the inherent geometry/fabric anisotropy and manifested by the anisotropic stress state. In addition, the degree of cross-anisotropy was defined and used to evaluate the cross-anisotropic properties of the geomaterials.

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 score0.072
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.168
Teacher spread0.162 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

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