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Record W2598290923 · doi:10.1061/9780784480489.010

Estimation of Shear Wave Velocity in Centrifuge Models

2017· article· en· W2598290923 on OpenAlexaboutno aff
Waleed El-Sekelly, Tarek Abdoun, Vicente Mercado

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugeShear (geology)Shear modulusShear velocityGeotechnical engineeringWave velocityGeologyAccelerationMechanicsMaterials scienceComposite materialPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Shear wave velocity is an essential parameter to characterize the dynamic behavior of the soil in small-scale samples, centrifuge models as well as field deposits. In this paper, the bender elements system in the centrifuge lab at Rensselaer Polytechnic Institute is used to characterize the shear wave velocity and the small strain shear modulus of dry and saturated Ottawa sand. It was found that the shear wave velocity of saturated sand is smaller than that of dry sand having the same relative density. Also, the paper describes an analytical system identification (SI) method that can be used in the absence of bender elements to estimate shear wave velocity from acceleration recordings. The SI and bender elements were found to be in very good agreement with each other confirming the validity of both techniques, which are fully independent.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.218
Teacher spread0.199 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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