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Contribution of Beam-Column Connections with Bolted Angles in the Reserve Capacity and Full-Scale Cyclic Testing

2018· article· en· W2787974894 on OpenAlexaffabout
Thierry Béland, Robert Tremblay, Joshua G. Sizemore, Larry A. Fahnestock, Cameron R. Bradley, Eric M. Hines

Bibliographic record

VenueKey engineering materials · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsStructural engineeringBeam (structure)StiffnessDuctility (Earth science)Column (typography)Moment (physics)EngineeringDeformation (meteorology)Frame (networking)Shear wallFull scaleMaterials scienceConnection (principal bundle)CreepMechanical engineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Partially restrained beam-column connections can be used in the gravity load system of building structures to develop moment frame action to enhance collapse prevention for low-ductility steel lateral systems. The benefit from such reserve lateral strength and stiffness is illustrated for a low-rise building with steel braced frames designed in accordance with Canadian provisions for seismic force resisting systems of the Conventional Construction category. Preliminary results from a comprehensive cyclic test program recently completed on beam-to-column joints with bolted double web angle acting with top and seat angles are presented. The test program included 23 full-scale beam-to-column sub-assemblages subjected to combined gravity shear forces and cyclic rotational demands. Experimental observations on the deformation patterns and failure modes are presented together with representative hysteretic moment-rotation responses of bolted double web angles without and with top and seat angles.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.197
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 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".

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Citations2
Published2018
Admission routes2
Has abstractyes

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