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Record W1924269279 · doi:10.1115/1.4031755

Fiber-Reinforced Elastomeric Bearings for Vibration Isolation

2015· article· en· W1924269279 on OpenAlexaff
James M. Kelly, Niel C. Van Engelen

Bibliographic record

VenueJournal of vibration and acoustics · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElastomerMaterials scienceStiffnessBearing (navigation)Structural engineeringVibration isolationVibrationFiberReinforcementExtensibilityComposite materialComputer scienceEngineeringAcoustics

Abstract

fetched live from OpenAlex

Fiber-reinforced elastomeric bearings were originally proposed as an alternative to conventional steel-reinforced elastomeric bearings for seismic isolation applications. The flexible fiber reinforcement is a light-weight and potentially cost saving alternative to steel reinforcement which is assumed rigid in the design process. The variety of fiber materials available also serves as an additional parameter for designers to tailor the vertical stiffness of the bearing. In this paper, the analytical solution for the vertical compression modulus of a rectangular elastomeric pad including the effects of bulk compressibility and extensibility of the fiber reinforcement is used to investigate the achievable decrease in vertical frequency. It is shown by an example that the extensibility of the fiber reinforcement can be used to significantly reduce the vertical stiffness in comparison to an equivalent steel-reinforced elastomeric bearing. The resulting decrease in the vertical frequency means that fiber-reinforced elastomeric bearings may have an advantage over steel-reinforced bearings in the vibration isolation of buildings.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.230
Teacher spread0.216 · 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 designBench or experimental
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

Citations25
Published2015
Admission routes1
Has abstractyes

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