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Record W4250130271 · doi:10.32920/ryerson.14654235.v1

Axial load behaviour of steel tube columns in-filled with various high performance concretes

2021· preprint· en· W4250130271 on OpenAlexaff
Katie Chu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceComposite materialTube (container)Crumb rubberStructural engineeringDeformation (meteorology)ReinforcementComposite numberNatural rubberEngineering

Abstract

fetched live from OpenAlex

This research concentrates on the axial load behaviour of circular, square and rectangular concrete filled steel tube (CFST) columns incorporating high-performance self-consolidating concretes such as ultra-high strength concrete (UHSC), engineered cementitious composite (ECC), lightweight concrete (LWC), and crumb rubber concrete (CRC). Seventy-four CFST specimens with varying slenderness, shape, concrete type and presence of internal bar reinforcements are tested experimentally under axial compression loading. The effect of these variables on axial load-deformation response, strain characteristics, failure modes, concrete confinement and axial strength are evaluated through experimental results. Performance of existing analytical/code based models for axial strength and concrete confined strength is evaluated. Concretes without coarse aggregates including UHSC proved less effective at enhancing axial strength of filled tube columns through confinement. In contrast, confinement in filled steel tube columns was found most effective with the use of concretes with coarse aggregates such as LWC and CRC.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.194
Teacher spread0.187 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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