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Record W1943592505 · doi:10.1139/t10-092

Influence of particle size on the correlation between shear wave velocity and cone tip resistance

2011· article· en· W1943592505 on OpenAlexafffundvenueabout
Mourad Karray, Guy Lefebvre, Yannic Éthier, Annick Bigras

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsHydro-QuébecÉcole de Technologie SupérieureUniversité de Sherbrooke
FundersHydro-Québec
KeywordsCone penetration testGeotechnical engineeringLiquefactionPenetration testShear (geology)CompactionWave velocityGeologyParticle-size distributionParticle sizeGranular materialStandard penetration testGrain sizePenetration (warfare)MathematicsGeomorphology

Abstract

fetched live from OpenAlex

The construction of the Péribonka dam involved deep compaction of its foundation using vibroflotation and dynamic compaction. Surface wave testing was used, in addition to classical tests (cone penetration tests (CPTs) and standard penetration tests (SPTs)) for the assessment of vibrocompaction. More than 900 shear wave velocity (V s ) and 1000 CPT profiles were obtained. This set of tests performed prior to and following vibrocompaction constitutes an important data bank, used in this study to establish a relationship between normalized shear wave velocity, V s1 , normalized tip resistance, q c1 , and mean grain size, D 50 . Using the Péribonka project data obtained on fairly coarse sands in conjunction with the Canadian Liquefaction Experiment (CANLEX) project data obtained on fine sands has confirmed the significant effect of particle-size distribution on the relationship between V s and q c . The paper proposes a correlation between V s1 , q c1 , and D 50 for uncemented and Holocene-age granular soils in continuity with the relation developed by Wride et al. from the CANLEX project.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.373

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 integrity0.0000.001
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 head0.018
GPT teacher head0.180
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

Citations56
Published2011
Admission routes4
Has abstractyes

Explore more

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