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Record W2402911790 · doi:10.1177/0021998315625101

Effect of geometric parameters on the behavior of bolted GFRP pultruded plates

2016· article· en· W2402911790 on OpenAlexaff
Thérèse AD Tajeuna, Frédéric Légeron, Sébastien Langlois, Pierre Labossière, Marc Demers

Bibliographic record

VenueJournal of Composite Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPultrusionFibre-reinforced plasticMaterials scienceStructural engineeringFinite element methodFailure mode and effects analysisConnection (principal bundle)Bolted jointComposite materialBearing capacityOrthotropic materialEngineering

Abstract

fetched live from OpenAlex

This paper presents the effect of geometric parameters on the behavior of bolted glass fibre reinforced polymer (GFRP) pultruded plates for civil engineering applications. After a literature review, results of an experimental analysis investigating the behavior of GFRP-to-steel single-lap bolted connections are presented. Then, a finite element analysis validated by experimental data is used to evaluate the effects of the end-distance, side-distance, pitch, and plate properties on the strength and failure mode of the connection. A critical examination of geometric recommendations proposed in design references is presented. Bearing failure caused by contact of the bolt on the GFRP plate is usually defined as the preferred failure mode. With highly orthotropic plate, this type of failure was found to be less likely to occur when loading is applied in the pultruded direction. The investigation showed that the minimum end-distance and pitch-distance recommended by design references usually produce a connection with the maximum capacity. However, it was found that the minimum side-distance recommended by these references does not necessarily lead to the maximum capacity for one bolt and for two bolt in a column connections.

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)0.001
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 score0.004
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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