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Record W2254279907 · doi:10.3390/fib4010004

Experiment-Based Sensitivity Analysis of Scaled Carbon-Fiber-Reinforced Elastomeric Isolators in Bonded Applications

2016· article· en· W2254279907 on OpenAlexafffund
Farshad Hedayati Dezfuli, M. Shahria Alam

Bibliographic record

VenueFibers · 2016
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaLG DisplayMcMaster University
KeywordsMaterials scienceElastomerComposite materialVulcanizationNatural rubberDissipationFlexibility (engineering)FiberSensitivity (control systems)Carbon fibersStructural engineeringComposite number

Abstract

fetched live from OpenAlex

Fiber-reinforced elastomeric isolators (FREIs) are a new type of elastomeric base isolation systems. Producing FREIs in the form of long laminated pads and cutting them to the required size significantly reduces the time and cost of the manufacturing process. Due to the lack of adequate information on the performance of FREIs in bonded applications, the goal of this study is to assess the performance sensitivity of 1/4-scale carbon-FREIs based on the experimental tests. The scaled carbon-FREIs are manufactured using a fast cold-vulcanization process. The effect of several factors including the vertical pressure, the lateral cyclic rate, the number of rubber layers, and the thickness of carbon fiber-reinforced layers are explored on the cyclic behavior of rubber bearings. Results show that the effect of vertical pressure on the lateral response of base isolators is negligible. However, decreasing the cyclic loading rate increases the lateral flexibility and the damping capacity. Additionally, carbon fiber-reinforced layers can be considered as a minor source of energy dissipation.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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