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Record W2145386145 · doi:10.1177/0021998308092207

On the Pin Bearing Behavior of Orthotropic Fiber Metal Laminates

2008· article· en· W2145386145 on OpenAlexaff
P.P. Krimbalis, C. Poon, Kamran Behdinan, Zouheir Fawaz

Bibliographic record

VenueJournal of Composite Materials · 2008
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOrthotropic materialMaterials scienceBucklingDelamination (geology)Structural engineeringGLAREIsotropyComposite materialBearing (navigation)Yield (engineering)Joint (building)Finite element methodComputer scienceOpticsGeologyEngineering

Abstract

fetched live from OpenAlex

Experiments enforcing a pin bearing loading configuration were performed on a fully orthotropic GLARE 4 variant. The protocol employed in such experiments stemmed from a similar methodology performed on quasi-isotropic GLARE variants though now incorporating a local measurement scheme using biaxial strain gauges rather than uniaxial ones. The aim of this local measurement was both the extraction of novel bearing yield strength values and the detection of buckling within the aluminum layers. The encouragement of delamination and buckling was key since they not only form an integral portion of the proposed yielding through delamination buckling (YDB) mechanism but in addition, the pin bearing configuration — which ipso facto, encourages the former(s) — has been identified in the literature as the most conservative and accurate means for analyzing joint behavior and collapse. Analytical calculations previously performed support the empirical findings and provide direct evidence for the hegemony of aluminum yield strength in joint collapse. The proposed and employed protocol has been shown to be effective across a comprehensive range of GLARE variants and may be extended analogously to other standardized testing methodologies.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

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 head0.020
GPT teacher head0.231
Teacher spread0.211 · 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 teacher head, 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

Citations3
Published2008
Admission routes1
Has abstractyes

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