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Record W3094966956 · doi:10.1139/cgj-2019-0460

Small strain shear modulus and damping ratio of two unsaturated lateritic sandy clays

2020· article· en· W3094966956 on OpenAlexvenueno aff
C.W.W. Ng, O.T. Bentil, Chao Zhou

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsSesquioxideSoil waterDamping ratioMaterials scienceShear modulusGeotechnical engineeringSuctionMicrostructureWater contentComposite materialMineralogyGeologySoil scienceMetallurgy

Abstract

fetched live from OpenAlex

In this study, resonant column tests carried out to investigate the influence of suction on the shear modulus and damping ratio of two compacted lateritic sandy clays from Ghana (GL) and Nigeria (NL) are reported. Each type of soil was tested under two confining pressures and at three suctions. The microstructure of the soils was also studied through a scanning electron microscope. It was found that the effects of suction on maximum shear modulus (G 0 ) are about 10% larger for GL than NL, mainly due to the existence of smaller aggregates in GL. Moreover, an increase in suction from 0 to 300 kPa for both soils resulted in a lower elastic threshold shear strain, different from the behaviour of other soils reported in the literature. The uniqueness of lateritic soils is likely attributable to their high sesquioxide content and much larger aggregates, which shrink upon an increase in suction. Drying of specimens from 0 to 300 kPa resulted in an increase of about 22% and 100% in initial damping ratio (D 0 ) for GL and NL, respectively. The difference in D 0 for GL and NL is attributed to larger aggregation of NL because of its higher iron sesquioxide content, leading to more cladding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.194
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2020
Admission routes1
Has abstractyes

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