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Record W4251600982 · doi:10.32920/ryerson.14654022.v1

Finite Element Modeling of Steel Plates Under Monotonic Loading

2021· preprint· en· W4251600982 on OpenAlexaff
Bilal Nasir Shamsaldin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of WindsorToronto Metropolitan University
Fundersnot available
KeywordsQuadrilateralStructural engineeringDisplacement (psychology)Yield (engineering)Fuse (electrical)Finite element methodDeformation (meteorology)Materials scienceMonotonic functionStress (linguistics)Composite materialEngineeringMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Steel plate fuses can be used as energy dissipating devices in earthquake-resistant structures. After an earthquake, the structure remains essentially elastic and only the deformed fuse require replacement. This report simulates the monotonic response of steel plate specimens. The effects of different inputs such as imperfection, shape and size of the fuse openings, and different meshing types on yield strength, deformation, stress distribution, and displacement are studied by using ANSYS Mechanical APDL. The study found that increasing imperfection increases displacement and decreases yield strength. It was also concluded that as the hole size in the steel plate is increased, the fuse yield strength is slightly increased to a point then is decreased. Double diamond shape showed better response in terms of displacement and stress distribution, this is because of the link shape formed by the two holes. Finer quadrilateral meshing method provide precise simulation results over longer time.

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: none
Teacher disagreement score0.763
Threshold uncertainty score0.830

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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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