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Record W2756397021 · doi:10.1002/cepa.328

10.31: Simplified analytical determination of the temperature distribution and the load bearing resistance of slim‐floor beams

2017· article· en· W2756397021 on OpenAlexaff
Matthias Braun, Dario Zaganelli, François Hanus, Renata Obiala, Louis‐Guy Cajot, Anthony Peirce

Bibliographic record

Venuece/papers · 2017
Typearticle
Languageen
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSlabThermal conductionHeat fluxStructural engineeringBearing (navigation)USableTransient (computer programming)MechanicsBeam (structure)Materials scienceEngineeringHeat transferComputer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

ABSTRACT This contribution presents an analytical method to determine the distribution of temperature within a steel section integrated into a concrete slab, which is subjected to a time‐dependent heat flux. The method developed is based on an analytical solution of the general heat equation for transient conduction simplified to be usable for the calculation of the load bearing resistance of the slim‐floor beam. This simplified analytical solution is validated by comparison against results obtained from numerical simulations. A possible extension of the developed method to fire protected sections is given.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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
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

Citations2
Published2017
Admission routes1
Has abstractyes

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