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Record W2606943777 · doi:10.1520/gtj20160128

Cyclic Direct Simple Shear Test to Measure Strain-Dependent Dynamic Properties of Unsaturated Sand

2017· article· en· W2606943777 on OpenAlexaboutno aff
Khoa Ngoc Le, Majid Ghayoomi

Bibliographic record

VenueGeotechnical Testing Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsShear modulusGeotechnical engineeringDirect shear testSaturation (graph theory)Materials scienceStiffnessShear stressComposite materialShear (geology)GeologyMathematics

Abstract

fetched live from OpenAlex

The dynamic properties of a clean sand under different degrees of saturation were investigated using a modified custom built Direct Simple Shear (DSS) apparatus at the University of New Hampshire. The specific characteristics of the DSS were presented, and the testing procedures were discussed. The device used the axis translation and tensiometric techniques to control the matric suction in the soil specimen. The investigation on F75 Ottawa Sand showed a decrease in shear modulus and an increase in damping by increasing the shear strain over the tested range of strains for various degrees of saturation: dry, saturated, and partially saturated. The modulus reduction in the applied range of medium shear strains regardless of the degree of saturation demonstrated the capability of the DSS in consistently capturing the changes of dynamic properties. Experimental results indicated that the matric suction can have a substantial effect on the stiffness of the soil. However, the extent of this effect may depend on the induced strain level of the effective stress in unsaturated soil. In addition, partially saturated specimens resulted in lower dynamic compression.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.246
Teacher spread0.210 · 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

Citations42
Published2017
Admission routes1
Has abstractyes

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