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Record W2163075062 · doi:10.1177/1077546311404267

Vibration control of Timoshenko beam traversed by moving vehicle using optimized tuned mass damper

2011· article· en· W2163075062 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 · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsOntario Tech UniversityConcordia University
Fundersnot available
KeywordsTuned mass damperTimoshenko beam theoryDamperBeam (structure)Finite element methodVibrationStructural engineeringVibration controlControl theory (sociology)Bridge (graph theory)EngineeringComputer sciencePhysicsAcousticsControl (management)

Abstract

fetched live from OpenAlex

The dynamic behavior of a combined bridge–vehicle system in which the bridge is modeled as a Timoshenko beam and the vehicle as a half-car planar model is investigated using the finite element method. The governing equations of motion of the Timoshenko beam with the attached tuned mass damper (TMD) traversed by a moving vehicle are obtained. Adesign optimization algorithm is developed in which the analysis module based on the derived finite element formulation has been combined with the optimization module using the sequential programming technique. The objective is to determine the optimum values of the parameters (frequency and damping ratios) of a TMD, in order to minimize the maximum frequency response of the beam midspan when traversed by a moving vehicle. Results obtained illustrate that by attaching an optimally TMD to the Timoshenko beam a significantly faster vibration control can be 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.560

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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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