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Record W4308972791 · doi:10.3390/buildings12111962

Modelling of Strengthened Steel Connections under Static and Cyclic Loading

2022· article· en· W4308972791 on OpenAlexaff
M. Khelifa, Amar Khennane, Marc Oudjène

Bibliographic record

VenueBuildings · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFibre-reinforced plasticMaterials scienceWeldingStructural engineeringComposite materialReinforcementHardening (computing)Parametric statisticsStrain hardening exponentIsotropyAdhesiveEngineeringLayer (electronics)

Abstract

fetched live from OpenAlex

The rehabilitation of steel structures with Fibre Reinforced Polymers (FRP’s) may appear less effective because they can be bolted or welded with steel plates that display the same mechanical properties. However, this technique has some unwanted consequences such as additional dead weight and an increased risk of corrosion. The aim of the proposed study, therefore, is to present a technique for modelling steel connections strengthened with FRP’s. Two types of composites: Carbon Fibre Reinforced Polymer (CFRP) and Glass Fibre Reinforced Polymer (GFRP) are considered. They are used to strengthen welded steel connections. The main objective consists in evaluating the effect of the reinforcement on the load-carrying capacity of these connections under monotonic and cyclic loadings. The steel is considered to behave in a linear elastic perfectly plastic fashion with isotropic strain hardening, and the FRP’s are assumed to behave linearly up to failure. The behaviour of the adhesive is modelled with the Cohesive Zone Model (CZM) available in Abaqus. Lastly, a parametric study is carried out to investigate the eventuality of strengthening connections made with I-sections, which are very common in practice.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.493

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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