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Record W2969813467 · doi:10.1139/cjce-2018-0485

Rectangular hollow section webs under transverse compression

2019· article· en· W2969813467 on OpenAlexafffundvenue
Jens Kuhn, Jeffrey A. Packer, YuJing Fan

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityCanadian Institute of Steel Construction
KeywordsChord (peer-to-peer)Structural engineeringFinite element methodTransverse planeBucklingCompression (physics)Ultimate tensile strengthBearing capacityEngineeringMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

An investigation is presented into full-width, rectangular hollow section (RHS) X-connections subject to transverse compression, including the effect of a compressive or tensile chord preload. A re-evaluation of world-wide experimental tests on full-width X-connections revealed considerable inaccuracy with current design recommendations, as well as significant discrepancies between them. A finite element study was hence conducted to further investigate the behaviour of such connections. A critical value of the bearing length-to-chord height ratio was found, where yielding failure of the chord webs turns into buckling failure, and this has been implemented in the subsequent design recommendation. The proposed design procedure is based on 350 finite element results, covering a wide range of chord sidewall slenderness values, bearing length values and chord stress ratios, as well as against a screened database of 125 experimental tests. The proposal is shown to offer excellent predictions and incorporates a simple reliability analysis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.159
Teacher spread0.155 · 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

Citations17
Published2019
Admission routes3
Has abstractyes

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