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Record W2917339039 · doi:10.1680/jgeot.18.p.017

Dynamic properties of a sand–nanoclay composite

2019· article· en· W2917339039 on OpenAlexaboutno aff
Felipe Ochoa‐Cornejo, Antonio Bobet, Cliff T. Johnston, Marika Santagata, Joseph V. Sinfield

Bibliographic record

VenueGéotechnique · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsThixotropyMaterials scienceConsolidation (business)Composite materialComposite numberPore water pressureShearing (physics)Grain sizeOverburden pressureStiffnessGeotechnical engineeringShear (geology)ModulusShear modulusGeology

Abstract

fetched live from OpenAlex

The paper describes the influence of 1–3% (by dry mass of sand) Laponite, a highly plastic synthetic nanoclay, on the dynamic properties of Ottawa sand, based on undrained resonant column tests. The effect of Laponite depends on the amount added, the confining stress and consolidation time. With 1% Laponite, there is an increase in the very small-strain shear stiffness at all confining stresses, which increases with extended consolidation time, reflecting the thixotropic nature of the nanoclay. Added Laponite also produces an increase in small-strain damping, an extension of the linear strain threshold; and it delays the generation of excess pore pressure, degradation of shear modulus and increase in damping with shear strain. These effects, which become more significant with increasing Laponite content, can be attributed to the impact of the clay on the fabric and grain-to-grain contacts. Evidence is provided that Laponite interferes with direct interaction between the sand grains, and the thickness of the clay layer present at the contacts appears to control the small-strain behaviour of the sand–Laponite mixtures. The formation of a gel-like pore fluid from the hydration of Laponite in the voids, and the degree to which it occupies the pore space, are responsible for the behaviour in the non-linear region.

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 score0.251
Threshold uncertainty score0.498

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 head0.004
GPT teacher head0.167
Teacher spread0.163 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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