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Record W2109014230 · doi:10.1177/0731684408091920

Mode II Delamination Toughness in Glass Fiber-Reinforced Polymers with Bridging Fibers and Stitching Threads

2008· article· en· W2109014230 on OpenAlexafffund
Chengye Fan, P.‐Y. Ben Jar, J. J. Roger Cheng, Peter Davies

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2008
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsUniversity of AlbertaInstitut Français de Recherche pour l'Exploitation de la Mer
KeywordsImage stitchingMaterials scienceComposite materialBridging (networking)Delamination (geology)Shear (geology)Fracture toughnessGlass fiberToughnessFiber pull-outFinite element methodDirect shear testStructural engineeringComposite laminatesOpticsComputer science

Abstract

fetched live from OpenAlex

This article presents results for mode II delamination resistance of fiber-reinforced polymers (FRP) using a recently developed internal notched flexure (INF) test. The study showed that the decrease of the compliance with the growth of delamination in the INF test was much less than that predicted by analytical or finite element analyses of the same configuration. The difference was mainly due to pronounced shear force interaction generated by bridging fibers and stitching threads between fracture surfaces. A new data-deducing method, named direct method with correction (DMC), was developed for establishing a delamination resistance curve (R-curve), which can take into account the effects of shear force interaction but does not require in situ measurement of crack growth length. The DMC was further examined using results from a series of INF tests with variation in test set-up configurations and types of glass fiber preform.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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Same venueJournal of Reinforced Plastics and CompositesSame topicMechanical Behavior of CompositesFrench-language works237,207