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Record W2018413916 · doi:10.1002/tal.273

Optimum design of pendulum-type tuned mass dampers

2005· article· en· W2018413916 on OpenAlexafffund
Rafik R. Gerges, B.J. Vickery

Bibliographic record

VenueThe Structural Design of Tall and Special Buildings · 2005
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l’Éducation, Gouvernement de l’Ontario
KeywordsTuned mass damperPendulumAccelerationControl theory (sociology)Structural engineeringDamperAerodynamicsOptimal designAerodynamic forceWhite noiseEngineeringPhysicsComputer scienceMechanicsClassical mechanicsMechanical engineering

Abstract

fetched live from OpenAlex

The equations of motion are derived for a translational single degree of freedom system equipped with a ‘pendulum-type’ tuned mass damper (TMD) under dynamic force and base acceleration excitations. The complex frequency response functions are obtained. Following response minimization procedures, the optimum parameters of the TMD under random white noise excitations are determined. The effect of the TMD in reducing the response is expressed in terms of an equivalent viscous damping. The optimum design parameters and the corresponding efficiency of the TMD under both wind and earthquake dynamic loads are presented in design charts. The effect of the structure inherent and aerodynamic damping on the optimum parameters is studied and simplified charts to account for such effect are provided. Moreover, a design chart for the over-optimum-damped TMDs is presented. The translational-type TMD is treated as a special case of the pendulum-type. Copyright © 2005 John Wiley & Sons, Ltd.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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 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

Citations94
Published2005
Admission routes2
Has abstractyes

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