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Record W4380449700 · doi:10.52202/069179-0417

FINITE ELEMENT ANALYSIS OF BUCKLING BEHAVIOR OF TIMBER ENCASED STEEL COMPOSITE COLUMNS SUBJECTED TO AXIAL LOAD

2023· article· en· W4380449700 on OpenAlexaff
Ying Gao, Feiyang Xu, Minghao Geng, Qibin Hu, Xinmiao Meng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsBucklingFinite element methodStructural engineeringMaterials scienceDuctility (Earth science)Composite numberParametric statisticsComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

Timber encased steel composite (TESC) columns provide a feasible application to large-scale and highrise structures. This paper investigates the axial load distribution and buckling behavior of TESC columns with embedded H-section steel through finite element (FE) analysis combined with parametric studies. The numerical results revealed that increasing the slenderness ratio could enhance the confinement effect of the timber in improving the maximum load. A larger proportion of timber area provided a more significant confinement effect in enhancing the ductility of the steel, and then a minimum area ratio for TESC columns considering the confinement effect of timber was determined.

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 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 score0.263
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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.

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
Published2023
Admission routes1
Has abstractyes

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