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Record W1983725011 · doi:10.1139/t06-099

Effect of sample torsional stiffness on resonant column test results

2007· article· en· W1983725011 on OpenAlexfundvenueno aff
Jyant Kumar, C.R.I. Clayton

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersPolar Knowledge Canada
KeywordsMoment of inertiaTorsion (gastropod)Damping ratioMaterials scienceStiffnessInertiaShear modulusStructural engineeringColumn (typography)ModulusMechanicsComposite materialAcousticsPhysicsEngineeringVibrationClassical mechanics

Abstract

fetched live from OpenAlex

A number of resonant column tests were conducted in torsion on aluminium cylindrical bars. By changing the length to diameter ratio of these specimens, the resonant frequency was varied between 52 and 332 Hz. The values of the shear wave velocity (VS) and damping ratio (D) determined from these tests were found to remain acceptable up to a resonant frequency of about 175 Hz. Thereafter, with an increase in the torsional stiffness of the specimen, there was a significant decrease in VSof up to 32.2%, corresponding to a resonant frequency of 332 Hz, and a considerable increase in the damping was noticed. An apparent increase in the mass polar moment of inertia of the driving mechanism was also observed at a greater resonant frequency. The study reveals that, even for a stiff specimen, it is possible using the resonant column tests to accurately determine the dynamic properties of the specimen, provided that the length to diameter ratio of the specimen is increased so that the resonant frequency remains smaller than 175 Hz.Key words: damping, dynamic testing, resonant column test, resonant frequency, shear modulus.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.274
Teacher spread0.262 · 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

Citations34
Published2007
Admission routes2
Has abstractyes

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