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Record W4245648463 · doi:10.32920/ryerson.14653419.v1

Nonlinear finite element analysis of profiled steel deck composite slab system under monotonic loading.

2021· preprint· en· W4245648463 on OpenAlexaff
Shubhangi Attarde

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStructural engineeringMaterials scienceFinite element methodComposite numberSlabDeckParametric statisticsNonlinear systemComposite constructionShear (geology)Monotonic functionComposite materialOrthotropic materialEngineeringMathematics

Abstract

fetched live from OpenAlex

This research concentrated on the nonlinear finite element (FE) modeling of one-way composite floor slab system comprising of profiled steel deck and two types of concrete namely, Engineered Cementitious Composites (ECC) and Self-Consolidating Concrete (SCC). Two FE models were developed based experimental results of composite slabs subjected to in-plane monotonic loading. The simulated load-deflection response, moment resistance, and shear bond capacity using two FE models were in reasonable good agreement with experimental results. The FE models were used in a comprehensive parametric study to investigate the effect of numerical model parameters such as mesh size, dilation angle, steel sheet-concrete interaction contact, material properties and composite slab span. In addition, FE models were used to determine shear bond parameters of ECC and SCC composite slabs that can be used for design purposes.

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)0.000
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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
Published2021
Admission routes1
Has abstractyes

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