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Record W2037195584 · doi:10.1139/l09-078

New design equations for channel shear connectors in composite beams

2009· article· en· W2037195584 on OpenAlexafffundvenue
Amit Pashan, M. U. Hosain

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of SaskatchewanCameco (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Illinois at Urbana-Champaign
KeywordsDeckStructural engineeringMaterials scienceFailure mode and effects analysisCable glandShear (geology)Compressive strengthComposite materialBeam (structure)Shear strength (soil)Geotechnical engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

This paper briefly summarizes the results of an experimental research project involving the testing of push-out specimens with channel shear connectors. The test program consisted of three series, each with 12 push-out specimens. In each series, six specimens had solid concrete slabs and the other six specimens had concrete slabs incorporating wide-ribbed metal deck with ribs parallel to the beam. The test parameters included the compressive strength of concrete and the length and web thickness of the channel shear connector. The test results showed that, for a given length of channel, the concrete strength dictates the failure mode. In specimens with higher strength concrete, failure was caused by the fracture of the channel web. Concrete crushing–splitting was the observed mode of failure in specimens with solid slabs when lower strength concrete was used. A concrete shear plane type of failure was observed in most of the specimens with metal deck slabs. The strengths of concrete used ranged from 21 MPa to a maximum of 35 MPa.

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)0.002
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.005

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 head0.015
GPT teacher head0.197
Teacher spread0.182 · 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
GenreMethods

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

Citations41
Published2009
Admission routes3
Has abstractyes

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