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Record W1484074344 · doi:10.15866/irece.v5i4.3091

Shear Capacity of Steel Plates with a Square Opening

2014· article· en· W1484074344 on OpenAlexaff
K.S. Sivakumaran, Bo Chen

Bibliographic record

VenueInternational Review of Civil Engineering (IRECE) · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStructural engineeringMaterials scienceBucklingStiffnessShear (geology)Finite element methodParametric statisticsDeflection (physics)Square (algebra)Composite materialEngineeringGeometryMathematics

Abstract

fetched live from OpenAlex

Steel plate elements are frequently encountered in structural applications, where thin plates are susceptible for local buckling at low loads, while thick plates may reach their strength limit. An opening of any size and shape on such structural plates may significantly affect the stiffness, strength, and the failure characteristic of the entire plate. The objective of this study is to quantify the shear capacity of plates containing a centrally placed square opening. The finite element method based numerical investigation considered the behavior of simply-supported rectangular steel plates, with and without a square opening and subjected to in-plane shear loading, experiencing buckling, post-buckling, and yielding until failure. The model employed representative material model, initial geometric imperfections and large deflection analysis. The investigation established the shear strength reduction factors due to opening of different parametric dimensions, and compared such results with the reduction factors based on the current cold-formed steel design code provisions. This paper establishes an enhanced shear strength reduction factor design equation, incorporating the impact of a centrally located square opening, for the shear capacity of un-stiffened steel plates.

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 score0.125
Threshold uncertainty score0.722

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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
Published2014
Admission routes1
Has abstractyes

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