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Record W3182575058 · doi:10.18280/rcma.310301

Failure Prediction of GFRP Pultruded Single-Lap Adhesive Joints

2021· article· fr· W3182575058 on OpenAlexvenueno aff
Francesco Marchione

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2021
Typearticle
Languagefr
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
Fundersnot available
KeywordsPultrusionFibre-reinforced plasticAdhesiveStructural engineeringLap jointComposite materialMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Adhesive bonding is experiencing a significant increase in its use in various industrial fields, thanks to the numerous advantages it offers, such as a better distribution of stresses in the resulting structure.This aspect is of importance for joints between composite substrates, which are not very resistant to concentrated stresses.The aim of this work is to present a simplified method for predicting the failure load of a single-lap adhesive joint.This theoretical model is applied to the results of an experimental study to verify the effectiveness of the adhesive bond between pultruded GFRP adherends.The results of shear tests on specimens assembled with different adhesives and subjected to different artificial ageing conditions are reported.The experimental results recorded by shear tests were processed by the simplified shear-slip model based on fracture energy.The obtained experimental results are compared with the theoretical values obtained using the analysis of the cohesive zone model theory.Then, a simple computational model is proposed for the evaluation and design of the ultimate load in single-lap adhesive joints.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.069
GPT teacher head0.261
Teacher spread0.191 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicMechanical Behavior of CompositesFrench-language works237,207