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THE COMPARATIVE ANALYSIS OF THE RESULTS OF REAL AND NUMERICAL EXPERIMENTS FOR DEFINING THE ULTIMATE BEARING CAPACITY OF LIGHT GAUGE STEEL STUDS “ATLANT”

2018· article· en· W2899809832 on OpenAlexaboutno aff
Galina G. Kashevarova, Pavel A. Kosykh

Bibliographic record

VenueInternational Journal for Computational Civil and Structural Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering Research and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMultilinear mapIsotropyNonlinear systemDeformation (meteorology)Structural engineeringGauge (firearms)Hardening (computing)Bearing capacityBearing (navigation)Scale (ratio)Computer scienceEngineeringMathematicsGeologyMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The article in question describes the results of comparison of numerical and real experiments, presented in the report of the Canadian laboratory “Bodycote” [1]. The paper depicts the test fixture, on witch six samples (two typical sizes) of light gauge steel stud “ATLANT” were tested. As a result, dependencies between bearing capacity and characteristic displacements were received. In order to repeat the experiment, the series of computer models were created with the software ANSYS. The computer models have complex nature: geometrical / physical nonlinearity was used. For accounting steel material nonlinearity, we used a multilinear model with isotropic hardening (MISO). The proper diagram was used for each typical size in accordance with the experiments on the tensile-testing machine. For the purpose of supporting condition and load modeling which are identical to the full-scale experiment, a couple of contact elements were used. The utilization of contact elements allowed us to consider friction between the sample and the supporting structures. One of the disadvantages of the full-scale experiment is the absence of measurements of the initial geometrical imperfections. For their consideration the use of probabilistic approach is suggested. This approach entails calculation of several models with different spread of initial imperfections. The initial geometrical imperfections with stochastic nature were included in the computing model. Parameters of distribution were based on the measurements of eighty-eight C-shaped members [8]. In the result of comparison, fine precision in terms of the ultimate bearing capacity and deformation pattern were established. According to expectation, the results of the full-scale experiments were found inside the fictitious “corridor”, created in accordance with the results of computer modeling.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.316
Teacher spread0.291 · 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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Citations1
Published2018
Admission routes1
Has abstractyes

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