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Record W4396722339 · doi:10.1080/13632469.2024.2345180

Viscoelastic Dampers for Vibration Control of Building Structures: A State-of-Art Review

2024· review· en· W4396722339 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 Earthquake Engineering · 2024
Typereview
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsMcGill University
Fundersnot available
KeywordsViscoelasticityDamperStructural engineeringVibrationVibration controlState (computer science)EngineeringTuned mass damperComputer scienceMaterials sciencePhysicsAcousticsComposite material

Abstract

fetched live from OpenAlex

Due to its high effectiveness and low cost, viscoelastic damper (VED) is a commonly used type of passive energy dissipation device to reduce structural vibrations and responses against earthquakes and strong winds. Over the past decades, scholars have developed new types of VEDs to be installed at different structural locations. These VEDs offer better post-disaster recoverability and smarter behaviors for structures. Nonetheless, existing efforts of various VEDs and the technologies supporting VEDs were seldomly summarized. This article presents a critical state-of-art review of the existing research on VEDs, hybrid VED devices, and the design methods for structures installed with VEDs. First, the VEDs are classified based on the design locations in building structures, including VEDs used as coupling beams and damping walls, installed in braces and beam-column joints, and used to connect parallel structures. In addition to these classic VEDs, the study presents the high-performance VEDs and the corresponding techniques, such as the combined usage with other materials and/or devices. Furthermore, as an important contribution to the presented work, various design methods for structures enhanced by VEDs were systematically summarized. These methods considered different evaluation parameters aiming at different design targets. Finally, this article identifies and highlights research challenges in the existing studies. Possible improvements that could be made in the future were also provided.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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