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Record W1998366731 · doi:10.1115/1.3085890

Optimal Vibration Suppression of Timoshenko Beam With Tuned-Mass-Damper Using Finite Element Method

2009· article· en· W1998366731 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of vibration and acoustics · 2009
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsOntario Tech UniversityConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTuned mass damperFinite element methodOptimal designTimoshenko beam theoryStructural engineeringVibrationBeam (structure)DamperHarmonicSequential quadratic programmingSensitivity (control systems)Control theory (sociology)EngineeringComputer scienceQuadratic programmingMathematicsPhysicsMathematical optimizationAcousticsElectronic engineering

Abstract

fetched live from OpenAlex

In this study, the structural vibration analysis and design of a Timoshenko beam with the attached tuned-mass-damper (TMD) under the harmonic and random excitations are presented using the finite element technique. A design optimization methodology has been developed in which the derived finite element formulation of a Timoshenko beam with the attached TMD has been combined with the sequential quadratic programming optimization algorithm to find the optimal design variables of TMD in order to suppress the vibration effectively. The validity of the developed optimal TMD system design strategy has been verified through illustrative examples, in which the structural response comparisons and the sensitivity analysis of the design parameters have been presented. The results were compared with those available in literatures and very close agreement was achieved.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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