MétaCan
Menu
Back to cohort
Record W2313372674 · doi:10.1520/gtj20130189

Shear Wave Velocity Measurement in the Centrifuge Using Bender Elements

2014· article· en· W2313372674 on OpenAlexaboutno aff
Waleed El-Sekelly, Anthony Tessari, Tarek Abdoun

Bibliographic record

VenueGeotechnical Testing Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersAgence Nationale pour le Développement de la Recherche Universitaire
KeywordsCentrifugeGeotechnical engineeringShear (geology)Wave velocityGeologySoil testShear velocityShear wavesSoil waterSoil scienceMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract Shear wave velocity is an important parameter for the design of geotechnical systems, particularly in seismically active areas. The availability of a reliable method for measuring shear wave velocity in centrifuge soil models is necessary in order to fully characterize the soil. The paper describes enhanced bender elements system developed at Rensselaer Polytechnic Institute. The fabrication process of bender elements in the laboratory is described in this paper. Also, the estimation of the shear wave velocity using the first arrival method is explained along with a study of near field presence in the results. To confirm the validity of the system, four centrifuge experiments were conducted on dry and saturated Ottawa F#55 sand. A comparison is performed between dry and saturated soil having the same relative density. Also, the effect of the direction of polarization of the shear wave is studied in the paper. The results are then compared to theoretical values estimated based on the literature and are found to match reasonably well; thus confirming the accuracy of the new system.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.076
GPT teacher head0.233
Teacher spread0.157 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueGeotechnical Testing JournalSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207