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Shear load capacity in masonry: international standards comparative analysis

2024· article· en· W4404020466 on OpenAlexaboutno aff
Vinícius Navarro Varela Tinoco, Klaus André de Sousa Medeiros, Guilherme Aris Parsekian

Bibliographic record

VenueRevista IBRACON de Estruturas e Materiais · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMasonryStructural engineeringShear wallDiagonalShear (geology)Finite element methodFlexural strengthShear strength (soil)EngineeringEuropean standardGeotechnical engineeringGeologyMathematicsTransport engineeringGeometry

Abstract

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Abstract Several structural models aim to predict the behavior of masonry walls. Those are included in technical standards strive to ensure the anticipation of the structural capacity, thereby assuring its safety. Different regions of the world encompass diverse parameters for predicting the lateral capacity of masonry members. This study sought to compare the Brazilian, Canadian, American, Australian, and European masonry standards regarding the shear and flexural capacity specification of walls. Equations derived from the standards were contrasted against finite element modeling results and to reliable formulation from the literature for predicting wall shear behavior. Additionally, an actualization is proposed to the Brazilian standard. The results revealed that while flexural capacity specifications result in comparable values across all standards, the Brazilian standard exhibited the least conservatism in the shear capacity, followed by the Australian, American, Canadian, and European standards. The diagonal shear was the primary mode of panel failure of the walls from the FE (Finite Element) modeling. The suggested adjustments to the Brazilian code, including considering the net area and correction of the initial shear strength (fvk0), result values close to the modeling results.

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 categoriesInsufficient payload (model declined to judge)
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 score0.390
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.259
Teacher spread0.242 · 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.

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
Published2024
Admission routes1
Has abstractyes

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Same venueRevista IBRACON de Estruturas e MateriaisSame topicMasonry and Concrete Structural AnalysisFrench-language works237,207