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Record W4308864577 · doi:10.5267/j.esm.2022.11.003

A study on the effect of thickness and spherical diagonal for LYP Infill Plate of shear walls

2022· article· en· W4308864577 on OpenAlexvenueno aff
Mohammad Mohammadiasl, Amir Baghban, Peyman Beiranvand, Hamidreza Babaali

Bibliographic record

VenueEngineering Solid Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInfillMaterials scienceStructural engineeringDiagonalSteel plate shear wallBucklingDissipationStiffnessShear (geology)Shear wallComposite materialZigzagPerpendicularGeometryEngineeringMathematics

Abstract

fetched live from OpenAlex

One of the most significant parameters which should be considered by all engineers is improving structures’ strength subjected to lateral load. Steel shear wall whose duty is to affect lateral load (wind and earthquake) is a wall which consists of shear part. Application of low yield point (LYP) steel in shear walls allows the employment of moderate and/or stocky infill plates with low yielding and high buckling capacities, which can result in enhanced buckling stability, serviceability, and energy dissipation capacity of such systems. Infill LYP plate is used to improve shear wall behavior which leads to enhancement of stiffness. In the present research, infill plate with spherical appendages is applied, and its impact on plate stiffness, cyclic behavior and energy absorption are investigated. The spherical diameter has been chosen respectively 10 and 20 cm distributed with two patterns (diagonal and plus form). The best performance is for a LYP plate with a 10 cm spherical diagonal pattern.

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 score0.011
Threshold uncertainty score0.445

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 head0.007
GPT teacher head0.209
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

Citations2
Published2022
Admission routes1
Has abstractyes

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