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Record W4398762430 · doi:10.1016/j.heliyon.2024.e31822

A simplified homogeneous approach for non-linear analysis of masonry infill panels under in-plane loads

2024· article· en· W4398762430 on OpenAlexaff
Zhichun Fang, Renyuan Wang, Pengfei Wu, Hui Sun, Mohammad Javad Moradi

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsInfillMasonryHomogeneousStructural engineeringPlane (geometry)GeologyEngineeringGeotechnical engineeringGeometryMathematicsPhysicsStatistical physics

Abstract

fetched live from OpenAlex

This paper aims to create a unified model that effectively combines continuous 2-dimensional elements and discrete components to capture the nonlinear characteristics and failure mechanisms of solid and perforated masonry infill panels. Given that masonry infill behavior is primarily influenced by shear deformations, an equivalent model is developed by using multiple small square panels arranged diagonally and interconnected by two-component springs, encompassing axial and shear behavior at their intersections. For the sake of simplicity, the divided panels are assumed to behave elastically, with plasticity concentrated only in the axial component of the connector springs. Plastic behavior in the boundary elements was considered to involve both flexural and shear plastic hinges to provide an accurate estimation of the entire infill panel's behavior. To validate this approach, the simplified model is benchmarked against eight experimental masonry infill panels surrounded by steel or reinforced concrete frames and with or without openings. The results including global behavior and crack pattern were compared with available numerical predictions based on finite element method from the literature in addition to experimental outcomes. Ultimately, this comparison demonstrated that the homogeneous model could effectively predict the non-linear lateral behavior of the panels and accurately forecast crack patterns. Additionally, the use of unidirectional non-linear springs and the appropriate arrangement of elastic panels significantly reduced both pre-processing and analysis 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: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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
Published2024
Admission routes1
Has abstractyes

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