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Record W2017759145 · doi:10.1061/9780784413357.083

Full-Scale Tests on Shear Connections of Composite Beams Under a Column Removal Scenario

2014· article· en· W2017759145 on OpenAlexaff
A. Jamshidi, Robert G. Driver

Bibliographic record

VenueStructures Congress 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStructural engineeringSlabDeflection (physics)Composite numberShear (geology)Failure mode and effects analysisMaterials scienceFraming (construction)Flexural strengthEngineeringGeotechnical engineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

In this research, the progressive collapse behaviour of a shear connection of a composite beam has been investigated experimentally. Steel shear/simple connections are widely used in composite gravity framing systems and their behaviour under conventional loading is well understood. However, their response and robustness under the column removal scenario is still largely unknown. While the slab itself can participate in maintaining the integrity of the overall floor system, its presence also amplifies the demand on the steel connections after experiencing initial flexural action. As the column pulls down progressively, which results in a large vertical deflection, the shear connection reaches its capacity under tension-dominant action. The contribution of the concrete slab to the steel connection demands plays an important role in determining the failure mode, load carrying capacity, and ductility of the connections. The main objective of the experimental program is to study the behaviour of steel shear connections in the presence of a concrete slab under a column removal scenario. The test results show the performance of the connection in terms of rotational ductility and ultimate load capacity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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