MétaCan
Menu
Back to cohort
Record W2322945721 · doi:10.1061/9780784413357.236

Thin-Walled, Cold-Formed, Steel-Welded Tube Design in a Long Span Dome

2014· article· en· W2322945721 on OpenAlexaff
A. Chatterjee, A. Algara, Francisco Castaño, Cristopher D. Moen

Bibliographic record

VenueStructures Congress 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsBucklingStructural engineeringWeldingMaterials scienceCold formingTube (container)Dome (geology)CurvatureSpan (engineering)Stub (electronics)RADIUSComposite materialEngineeringGeologyComputer scienceMathematicsGeometry

Abstract

fetched live from OpenAlex

Experiments are performed to validate the American Iron and Steel Institute's Direct Strength Method (DSM) for cold-formed steel design applied to a unique, thin-walled, 3-radius, cold-formed steel oval tube. The tube segments make up a structural system for long span domes with surface areas over 20,000 m2. While it is expected that the dome design will be governed by overall global buckling under dead load, the compressive strength of the individual tubes experiencing local buckling is also checked. Strength prediction methods are limited for thin-walled steel tubes, especially for non-rectangular cross-sections, leaving engineers with few validated approaches for predicting axial capacity including local buckling. Stub column experiments are conducted to evaluate local buckling behavior and tested capacities are compared to DSM predictions employing finite strip elastic buckling analysis. It is confirmed with elastic buckling studies and the experiments that local buckling tendencies are reduced with radial curvature.

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)0.000
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.219
Teacher spread0.210 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueStructures Congress 2014Same topicStructural Load-Bearing AnalysisFrench-language works237,207