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Numerical Investigation of Fillet Welds in HSS-to-Rigid End-Plate Connections

2017· article· en· W2757737954 on OpenAlexafffund
Kyle Tousignant, Jeffrey A. Packer

Bibliographic record

VenueJournal of Structural Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFillet (mechanics)Fillet weldWeldingStructural engineeringFinite element methodParametric statisticsMaterials scienceEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

This paper presents a finite-element (FE) investigation on the behavior of fillet-welded hollow structural section (HSS) rigid end-plate connections, wherein the entire weld length is effective. The FE models are validated by comparison to 33 experimental tests and a parametric study is then performed with 73 numerical tests to evaluate the effect of weld size, HSS branch wall slenderness, and branch inclination angle on fillet-weld strength. The inherent problem with single-sided fillet welds to a tension-loaded element is illustrated. A reliability analysis determined that the directional strength-increase factor for fillet welds in North America leads to inadequate values of the safety index for joints to both circular and rectangular HSSs, especially for connections with large welds. Hence, an alternative yet safe method for estimating the strength of fillet welds to HSS, based on weld size, is proposed. An expression for the fillet-weld size required to develop the yield strength of a 90° HSS branch member is derived.

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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