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Record W4392199438 · doi:10.18280/mmep.110217

Numerical Study of Performance of Alternate Closed Opening Castellated Beam Using Double Steel Channel Shapes

2024· article· en· W4392199438 on OpenAlexvenueno aff
Nihad Yaseen Abbas, Ahmad Jabbar Hussain Alshimmeri

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersUniversity of Baghdad
KeywordsStructural engineeringChannel (broadcasting)Beam (structure)Materials scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This numerical study presented and analysed a new technic of castellated steel beam "Alternate Closed Opening Castellated Beam" applied only to double steel channel shapes connected back to back.ABAQUS/2019 program employed to create modelling and analysis for two groups of models with the same length and conditions of loading.The first group has three noncomposite beam models: the first reference model without castellated, the second model with normal castellated and the third model with new castellated, also the second group has three composite beam models: the first reference model without castellated, second model with normal castellated and the third model with the new castellated.According to the analysis results; For the first noncompsite group, ultimate load for the second and third models increased by 8.84% and 16.63%, respectively, compared to the reference model, with local buckling in the top flanges under concentrated loads and lateral-torsional buckling as the failure modes.For the second composite group, the ultimate load for the second and third models increased by 41.83% and 62.19%, respectively, compared to the first reference model, with the flexural mechanism as the main failure mode.This enhancement in the load-carrying capacities of the new Alternate Closed Opening Castellated Beams is due to the limit states mode of failures resulting from web holes.

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 score0.122
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.234
Teacher spread0.197 · 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
Published2024
Admission routes1
Has abstractyes

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