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Record W3204067247 · doi:10.2749/ghent.2021.2044

Numerical study of a blind bolted connection between an aluminum bridge deck and steel girders

2021· article· en· W3204067247 on OpenAlexaffabout
Soufiane el Ogri, Charles-Darwin Annan, Pampa Dey

Bibliographic record

VenueReport · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFrettingDeckGirderStructural engineeringFinite element methodBridge (graph theory)EngineeringAluminiumMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This article concerns the behaviour of two blind bolt types, Ajax One side and Blind Oversize Mechanically (BOM), used to connect a multi-void aluminum bridge deck on its supporting steel girders. An extensive numerical simulation by FEM was performed to evaluate the connection behaviour against the Canadian Highway Bridge Design standard CSA S6-19. The main objective was to examine the assembly against fretting and quantify its impact at the contact zone over several load cycles. A special numerical model was developed for the prediction of fretting, and validated with analytical results and other observations reported in the literature. The model was used to analyze the fretting for each bolt at the surface of contact between the bolt head and the aluminum plate. Results of the study revealed that the blind bolts will lead to a few micrometers of wear, while for the standard bolt, a probable crack developments associated with minor wear may occur at the contact area.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.290
Teacher spread0.246 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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