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Record W4236358594 · doi:10.1139/cjce-2021-0057

Reinforcement schemes for cold-formed steel joists with a large web opening in the flexural zone: an experimental investigation

2021· article· en· W4236358594 on OpenAlexaffvenue
Sandesh R. Acharya, K.S. Sivakumaran

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsMcMaster UniversityStantec (Canada)
Fundersnot available
KeywordsReinforcementFlexural strengthStructural engineeringBridging (networking)JoistCompression (physics)Ultimate tensile strengthMaterials scienceSlabComposite materialEngineeringComputer science

Abstract

fetched live from OpenAlex

The objective of this investigation was to develop an economical and an efficient reinforcement scheme for large web openings in cold-formed steel (CFS) joists in flexural zones that would restore the original moment resistance of the section. This investigation considered commonly used floor joists in North American CFS construction. Tests on joists webs with no openings and with large circular and square openings located at the mid-depth revealed that large openings can reduce the flexural strength by 23%. A reinforcement scheme was established consisting of the screw fastening of bridging channels [38.1 mm × 12.7 mm (1.5″ × 0.5″)] with the same thickness as the receiving joist, along the compression and the tensile edges of the opening, using a screw spacing of 31.75 mm. Three identical tests utilizing the proposed reinforcement scheme restored the corresponding flexural strengths of the CFS section under consideration, and the failure locations were outside the reinforced opening regions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2021
Admission routes2
Has abstractyes

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