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Record W3003301424 · doi:10.12989/scs.2018.28.4.483

Shear capacity equation for channel shear connectors in steel-concrete composite beams

2018· article· en· W3003301424 on OpenAlexaboutno aff
Masoud Paknahad, Mahdi Shariati, Yadollah Sedghi, Mohammad Bazzaz, Majid Khorami

Bibliographic record

VenueSteel and Composite Structures · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringParametric statisticsFinite element methodShear (geology)Composite numberMaterials scienceShear strength (soil)EngineeringComposite materialGeologyMathematics

Abstract

fetched live from OpenAlex

In this research the effect of high strength concrete (HSC) on shear capability of the channel shear connectors (CSC) in the steel concrete composite floor system was estimated experimentally and analytically. The push-out test was carried out for assessing the accurateness of the proposed model (nonlinear and finite element model) for the test specimens. A parametric analysis was conducted for predicting the shear capacity of the connectors (CSC) in the HSC. Eight push-out specimens of different sizes of channels with different high strength concrete were tested under the monotonic loading system. The aim of this study was to evaluate the efficacy of the National Building Code of Canada (NBC) of Canada for analysing the loading abilities of the CSC in the HSC. Using the experimental tests results and verifying the finite element results with them, it was then confirmed by the extended parametric studies that the Canadian Design Code was less efficient for predicting the capacity of the CSC in the HSC. Hence, an alternative equation was formulated for predicting the shear capacity of these connectors during the inclusion of HSC for designing the codes.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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 designBench or experimental
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

Citations78
Published2018
Admission routes1
Has abstractyes

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