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Record W4317827748 · doi:10.21608/asge.2022.280711

Shear Wave Velocity Measurements of Granular Soils using the P-Rat

2022· article· en· W4317827748 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Advances in Structural and Geotechnical Engineering · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWave velocityShear (geology)Soil waterGeotechnical engineeringGeologyMaterials scienceComposite materialSoil science

Abstract

fetched live from OpenAlex

The small strain shear modulus, Gmax, is considered a fundamental design parameter in many geotechnical applications for soils under dynamic loads. Gmax can be estimated from both in situ and laboratory techniques. The piezoelectric ring-actuator technique (P-RAT) was developed at the geotechnical laboratory of Université de Sherbrooke (QC, Canada) to measure the shear wave velocity (Vs) and accordingly Gmax. This technique can be incorporated into different conventional apparatuses (e.g., triaxial, oedometer). This paper represents a description of the development of P-RAT to estimate Vs with a suitable accuracy as well as the developed interpretation method of output signals during Vs measurements. In addition, the Vs results of three granular soils were measured and a correlation between the void ratio (e) and Vs was then established. The obtained results showed compatibility with different Vs-e correlations proposed in the literature.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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