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Record W2916352450 · doi:10.1520/gtj20170322

Dynamic Behavior of a Granular Medium Subjected to Resonant Column Tests: Application to Ottawa Sand

2019· article· en· W2916352450 on OpenAlexaboutno aff
Hernán Patiño, Eliana Martínez, Rubén Galindo

Bibliographic record

VenueGeotechnical Testing Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringGranular materialColumn (typography)Materials scienceGeologyEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Abstract This research is based on the results of 120 determinations of the shear modulus (G) of a saturated granular medium (20-40 Ottawa sand) for different conditions of relative density (Dr), effective consolidation pressure (σ’c), and level of torsional excitation (Te) obtained through the measurement of the resonant frequency (Fr) in resonant column equipment. The tests were performed with relative density values equal to 20, 40, 60, and 80 %; effective consolidation pressures of 50, 100, 150, 200, 250, and 300 kPa; and torsional excitations of 0.025, 0.05, 0.1, 0.2, and 0.4 V. The proposed experimental program is described in detail, in addition to the systematic process of analysis to properly study and interpret the results obtained for samples subjected to cyclic loading. In general, the results indicate that it is possible to establish very simple empirical functions of the resonant frequency as a function of the angular strain and of the effective consolidation pressure. It is concluded that is statistically significant to consider the shear modulus and the resonant frequency related by a constant value M for each type of soil. In this sense, a methodology is proposed herein to be able to obtain from a very small number of tests the variation trends of the shear modulus as a function of the angular strain for different effective consolidation pressures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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.

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

Citations9
Published2019
Admission routes1
Has abstractyes

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