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Record W2801891186 · doi:10.1139/cjce-2017-0499

Lateral torsional buckling of welded wide flange beams under constant moment

2018· article· en· W2801891186 on OpenAlexafffundvenueabout
Md. Imran Kabir, Anjan K. Bhowmick

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsConcordia University
FundersAlbert Einstein College of Medicine, Yeshiva UniversityConcordia UniversityCanadian Institute of Steel Construction
KeywordsFlangeWeldingStructural engineeringBucklingResidual stressMoment (physics)Finite element methodEurocodeConstant (computer programming)EngineeringMaterials sciencePhysicsMechanical engineeringComputer scienceComposite materialClassical mechanics

Abstract

fetched live from OpenAlex

The structural steel design specification in Canada, CSA S16-14, uses the same equations for the design of rolled and welded shape beams for lateral torsional buckling (LTB). A recent study has shown that the current design equations might overestimate the capacity of welded wide flange (WWF) beams. This paper evaluates the performance of the current design equations in providing LTB capacities of welded I-shape beams (WWF beams). An extensive finite element (FE) analysis is performed for simply supported WWF beams subjected to constant moment. In total, 256 FE models are analyzed and it is observed that both CSA and AISC overestimate the LTB capacity of welded I-shape beams by as much as 37%, particularly when residual stress measured at Lehigh University is used in the analysis. Also, the Eurocode is found to be conservative and the proposed equation by MacPhedran and Grondin in 2011 provides better predictions of LTB strengths of WWF shapes than the current CSA approach.

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: Bench or experimental · Consensus signal: none
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)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.179
Teacher spread0.173 · 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 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

Citations8
Published2018
Admission routes4
Has abstractyes

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