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Record W2160956630 · doi:10.1177/1077546312468461

Vibration suppression of curved beam-type structures using optimal multiple tuned mass dampers

2012· article· en· W2160956630 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.

Bibliographic record

VenueJournal of Vibration and Control · 2012
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsOntario Tech UniversityConcordia University
Fundersnot available
KeywordsTuned mass damperVibrationControl theory (sociology)StiffnessDamperStructural engineeringBeam (structure)Vibration controlGenetic algorithmNormal modePosition (finance)Optimal designEngineeringComputer scienceMathematicsPhysicsAcousticsMathematical optimization

Abstract

fetched live from OpenAlex

This paper established a thorough optimization procedure of the multiple tuned-mass-damper system to suppress the vibration levels of the curved beam-type structures with multiple vibration dominant modes. A hybrid optimization methodology, which combines the global optimization method based on the Genetic Algorithm and the local optimization method based on Sequential Quadratic Programming, has been developed. The established hybrid optimization procedure is then utilized to find the optimum values of the design parameters, namely, the spring stiffness, damping factor and the position of the attached tuned-mass-damper systems, in order to suppress the vibration amplitude either at a particular mode or at several modes simultaneously.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.377

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 communication0.0000.001
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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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