MétaCan
Menu
Back to cohort
Record W2344217745 · doi:10.1139/cjce-2015-0285

Experimental investigation of multi-material aluminum-to-steel and glass fiber reinforced polymer-to-steel bonded and bolted–bonded connections

2016· article· en· W2344217745 on OpenAlexafffundvenue
Thérèse A.D. Tajeuna, Frédéric Légeron, Sébastien Langlois, Pierre Labossière, Marc Demers

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCentre québécois de recherche et de développement de l’aluminium
KeywordsMaterials scienceComposite materialAdhesiveFibre-reinforced plasticLap jointEpoxyRivetBolted jointDeformation (meteorology)MethacrylateGlass fiberJoint (building)Ultimate tensile strengthStructural engineeringPolymerLayer (electronics)Finite element method

Abstract

fetched live from OpenAlex

Through an experimental study, this paper describes the behavior of single-lap bonded and bolted–bonded connections for configurations with minimum geometric parameters proposed in design references. Two types of multi-material connections are considered: glass fiber reinforced polymer (GFRP)–steel and aluminum–steel. At first, the behavior of bonded connections using methacrylate and epoxy adhesives is evaluated. Then, the contribution of adhesive in bolted connections is investigated. Test results show that on bonded joints, failures mostly occur at the substrate to adhesive interface. Sanding the GFRP plate was found to improve the connection strength. Despite their lower elastic modulus, methacrylate adhesives with larger capacity to undergo plastic deformation provide better strength than other methacrylate and epoxy adhesives. For bolted–bonded joints, the adhesive was found to improve the elastic behavior and the strength of GFRP–steel joints while its effect for aluminum–steel joints was not apparent due to reduced bonded surface and the high strength performance of the bolted plates.

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 score0.006
Threshold uncertainty score0.806

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)0.0000.000

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 head0.205
Teacher spread0.193 · 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

Citations4
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicMechanical Behavior of CompositesFrench-language works237,207