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Model of the Shear Behavior of Unbonded Fiber-Reinforced Elastomeric Isolators

2014· article· en· W2021125423 on OpenAlexafffund
Niel C. Van Engelen, Michael J. Tait, Dimitrios Konstantinidis

Bibliographic record

VenueJournal of Structural Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsStructural engineeringIsolatorStiffeningStiffnessMaterials scienceFlexural rigidityShear (geology)Rollover (web design)ReinforcementElastomerParametric statisticsComposite materialEngineeringComputer scienceMathematics

Abstract

fetched live from OpenAlex

In this paper, an analytical model is developed and used to predict the horizontal behavior of unbonded fiber-reinforced elastomeric isolators (FREIs). An advantageous feature of unbonded FREIs is the ability to undergo rollover due to the lack of flexural rigidity of the reinforcement and the unbonded support conditions of the isolator. The rollover causes a characteristic softening, followed by stiffening, as the initially vertical faces of the isolator come into contact with the upper and lower supports—defined as full rollover. The force-displacement relationship is modeled by dividing the isolator into three sections: a central section, which experiences pure shear, and two rollover sections, which experiences combined shear and bending. The displacements of the rollover section are used to establish the curved deformed profile and predict full rollover. The model is evaluated with experimental data from four unbonded FREI designs. A parametric study is conducted to determine the influence of the aspect ratio and bending stiffness of the composite fiber reinforcement matrix on the horizontal force-displacement relationship.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.194
Teacher spread0.187 · 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 designSimulation or modeling
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

Citations48
Published2014
Admission routes2
Has abstractyes

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