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Record W2950828401 · doi:10.1139/cjce-2018-0804

Axial strength of steel circular hollow section (CHS) X-joints strengthened by flanged larger pipe

2019· article· en· W2950828401 on OpenAlexvenueno aff
Peter Gerges, Sameh Gaawan, Ashraf Osman

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsChord (peer-to-peer)BraceFlangeStructural engineeringStiffeningParametric statisticsWeldingStiffnessFinite element methodMaterials scienceComposite materialEngineeringMathematicsComputer science

Abstract

fetched live from OpenAlex

In steel design, enhancing the structural joints’ capacity is considered a challenge that faces the designer. This challenge becomes more difficult when it comes to enhancing the capacities of circular hollow section (CHS) joints due to their closed nature that complicates the strengthening process. Recent research related to strengthening T-joints by utilizing two outer hollow ring flanges welded to additional pipe showed that this technique can significantly improve the joints’ strength. In this study, the utilization of this technique is extended for enhancing the axial strength of CHS X-joints. In this regard, a parametric study using finite element models was carried out to investigate the different design aspects that might affect the behavior of strengthened X-joints. The examined parameters included, the ring flange diameter, the stiffening pipe thickness and length for different brace diameter-to-chord diameter ratios and chord diameter to double chord thickness ratio. The results demonstrated that these strengthened X-joints gained significant axial strength that reached up to three times the axial strength of the unstrengthened joints. Guidelines for proper detailing of such strengthening scheme were provided. Finally, an equation that estimates the axial strength of strengthened joints was established based on the achieved results.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.161
Teacher spread0.157 · 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 teacher head, not a consensus.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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