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Record W3128777719 · doi:10.1177/1077546320984305

Vibration suppression of structures using tuned mass damper technology: A state-of-the-art review

2021· review· en· W3128777719 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 · 2021
Typereview
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsOntario Tech UniversityConcordia University
FundersNational Natural Science Foundation of China
KeywordsTuned mass damperDamperVibrationVibration controlStructural engineeringEngineeringControl theory (sociology)Realization (probability)Computer sciencePhysicsAcousticsMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

To date, considerable attention has been paid to the development of structural vibration suppression techniques. Among all vibration suppression devices and techniques, the tuned mass damper is one of the most promising technologies due to its mechanical simplicity, cost-effectiveness, and reliable operation. In this article, a critical review of the structural vibration suppression using tuned mass damper technology will be presented mainly focused on the following four categories: (1) tuned mass damper technology and its modifications, (2) tuned mass damper technology in discrete and continuous structures (mathematical modeling), (3) optimization procedure to obtain the optimally designed tuned mass damper system, and (4) active tuned mass damper and semi-active tuned mass damper with the practical realization of the tuned mass damper technologies.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.016
GPT teacher head0.274
Teacher spread0.257 · 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