MétaCan
Menu
Back to cohort
Record W4399527761 · doi:10.1061/jsendh.steng-12762

RHS Cross-Connections with Fully Offset Branches in Tension

2024· article· en· W4399527761 on OpenAlexaff
Xiao Ding Bu, Vartkes Davidian, Jeffrey A. Packer, Wei Li

Bibliographic record

VenueJournal of Structural Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOffset (computer science)Tension (geology)Computer scienceStructural engineeringMaterials scienceEngineeringComposite materialProgramming language

Abstract

fetched live from OpenAlex

This paper primarily presents an investigation into rectangular hollow section (RHS) cross- (or X-) connections with the branches fully laterally offset and loaded in axial tension. A set of 10 full-scale experimental specimens, carefully fabricated with noncritical welds, are tested in the laboratory, and the results are used to validate nonlinear finite-element models. To enhance the range of available data, the calibrated models are varied in an expanded parametric numerical study. A chord failure limit-state model based on a yield-line mechanism, which was identified in prior research on branch compression loading, is verified herein for branch tension loading. Furthermore, the limit state of branch failure is investigated by means of the combined experimental and numerical database produced. Design recommendations for laterally offset cross-connections loaded under branch tension, based on chord and branch failure modes, are presented. The application of current design recommendations for traditional cross-connections (with centered branches) to connections with full-width branches and laterally offset branches is also evaluated.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.212
Teacher spread0.208 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Structural EngineeringSame topicStructural Load-Bearing AnalysisFrench-language works237,207