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Record W4367171879 · doi:10.18280/mmep.100228

Strengthening of Concrete-Filled Double Skinned Circular Steel Tubular (CFDSCT) Column: A Review Study

2023· review· en· W4367171879 on OpenAlexvenueno aff
Ahmed Dalaf Ahmed, Entidhar Al-Taie

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typereview
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsColumn (typography)Structural engineeringMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Due to its beneficial characteristics, such as its high load carrying capacity, good seismic resistance, fire resistance, high ductility, and quick construction, a concrete filled double skinned steel circular tubular (CFDSCT) column is a structural member that is frequently used in high-rise buildings.Many studies have proved that the factors controlling the bearing capacity of this type of column are column diameter-to-tube thickness (D/t), column length-to-diameter (L/D), central void ratio (χ), the yield of steel tubes (fy), and concrete strength (fc).In this study, the enhancement of the load-bearing capacity of the CFDST column was highlighted by adding some external details and changes to its structure.These external details and changes gave the structure more confinement and improved contact zoon between concrete and tubes.The differences between the previous test results were significant; a good additional improvement in compressive strength and bond strength reached 51% and 225%, respectively, higher than the conventional CFDSCT.This improvement was achieved by some changes in the outer tube structure or by using external details on the outer and inner tubes.This review also showed how the load-bearing capacity of the CFDSCT column could be improved if the best results from the above factors were added to the CFDSCT 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.061
GPT teacher head0.272
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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