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Record W2755895100 · doi:10.1002/cepa.416

12.16: Numerical investigation of the local buckling behaviour of high strength steel circular hollow sections

2017· article· en· W2755895100 on OpenAlexfundno aff
Andrea Toffolon, Andreas Taras

Bibliographic record

Venuece/papers · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersImperial College LondonUniversité Laval
KeywordsBucklingMaterials scienceHigh strength steelComposite materialStructural engineeringEngineering

Abstract

fetched live from OpenAlex

ABSTRACT The introduction of high‐strength steel (HSS) hollow sections with yield strengths of fy=690 MPa and beyond in the construction sector is currently made more difficult by a lack of knowledge on the specific local and local+global buckling behaviour of slender HSS cylindrical hollow‐sections. With rising yield strength, buckling phenomena become more relevant. Eurocode 3 design provisions classify CHS as “slender \ class 4” at a diameter to thickness ratio of D\t=90·∊2, with ∊2=235\fy. For HSS with fy=690 MPa and beyond, the majority of cold‐formed and many hot‐finished CHS will be class 4 and should be designed accordingly. However, no straightforward rules for the design of class 4 HSS CHS are found in EC3. This paper discusses initial numerical studies on the specific local buckling behavior of HSS CHS sections. The study represents the initial steps in the recently initiated RFCS research project “HOLLOSSTAB”, during which new design rules for HSS hollow sections are developed on the basis of an “Overall Interaction Concept” (OIC). This concept – similarly to the Direct Strength Method (DSM) used in North America for the design of cold‐formed steel open cross‐sections – makes use of the results of (numerical) linear buckling analyses (LBA) for the whole member to determine the slenderness and consequently an “overall” buckling reduction factor. The paper discusses how this approach fits into the general framework of buckling design checks for cylindrical structures, discusses existing rules and their implications for HSS CHS, shows numerical results and introduces initial design proposals based on the OIC 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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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