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An Evaluation of Design Code Expressions for Estimating in-plane Shear Strength of partiallY Grouted Masonry Walls

2014· article· en· W2316395969 on OpenAlexaboutno aff
Reza Hassanli, Mohamed A. ElGawady, Julie E. Mills

Bibliographic record

VenueAustralian Journal of Structural Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMasonryStructural engineeringUltimate tensile strengthGeotechnical engineeringShear strength (soil)Shear (geology)Compressive strengthShear wallGeologyBuilding codeShear stressUnivariateEngineeringMultivariate statisticsMaterials scienceMathematicsComposite materialStatisticsSoil water

Abstract

fetched live from OpenAlex

AbstractThis paper aims to evaluate the in-plane shear strength expressions of partially grouted masonry (PGM) walls in four international design codes, including masonry design codes of practice in the United States, New Zealand, Canada and Australia. Experimental results of 89 partially grouted masonry walls that displayed shear failure were collected from published research. The shear strengths of the walls in the database were calculated using the different code equations and compared with those from the experimental results. In addition, the parameters that influence the shear strength of the walls, including masonry tensile strength, level of axial compressive stress, wall aspect ratio, and the amount and spacing of vertical and horizontal reinforcement are studied. Both univariate and multivariate regression analysis have been employed to investigate the effect of each parameter on the accuracy of the code shear strength predictions. This study illustrates poor correlation between code predictions ...

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)0.001
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 score0.069
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.275
Teacher spread0.244 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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