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Record W1979873903 · doi:10.1260/1369-4332.15.1.107

Equivalent RHS Approach for the Design of EHS in Axial Compression or Bending

2012· article· en· W1979873903 on OpenAlexaff
Jeffrey A. Packer, Xiao‐Ling Zhao

Bibliographic record

VenueAdvances in Structural Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBuckleStructural engineeringBendingCompression (physics)Section (typography)Minor (academic)EngineeringFlexural strengthMaterials scienceMechanical engineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

The use of steel elliptical hollow sections (EHS) has grown steadily, but the implementation of EHS has been done without appropriate design guidelines or procedures. A pragmatic objective has been to develop a section conversion method to transform the EHS into an equivalent shape for which design equations and guidelines already exist. This paper further explores the equivalent RHS (rectangular hollow section) approach for member design. By using the equivalent RHS approach to convert the EHS into an equivalent RHS, the validity of existing RHS cross-section classification limits and member design equations are demonstrated for EHS columns subjected to axial compression that buckle about the major axis and the minor axis, and EHS beams subjected to bending about the major axis and the minor axis. The equivalent RHS approach is a single consistent and rational procedure, and it is shown to be a viable method for designing EHS compression members and flexural members.

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)0.001
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.025
GPT teacher head0.277
Teacher spread0.252 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

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