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Record W2967706569 · doi:10.1139/cjce-2018-0426

The effect of material type and joint thickness on wall behavior in conventional masonry walls

2019· article· en· W2967706569 on OpenAlexvenueno aff
Başak Zengin, Burak Toydemir, Ali Koçak

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMasonryJoint (building)Materials scienceStructural engineeringMasonry veneerMaterial propertiesComposite materialEngineering

Abstract

fetched live from OpenAlex

Masonry walls are systems that are typically preferred in conventional structures. The complex structure of masonry walls makes it harder to determine wall behavior; however, wall behavior is affected by the types of material used in conventional walls and the way in which joints interact with masonry units. The aim of this study is to investigate the effect of joint thickness and material factors on wall performance. To accomplish this, materials used in historical and conventional structures are preferred. This study involved hollow bricks, clay bricks, NHL 3.5, and CEM 32.5. Three different joint thicknesses were used to set up 12 different combinations. Damage estimation of the wall was carried out by moving a live load under a constant vertical load. Different materials of different joint thicknesses and mechanical properties were studied for their effects on the wall. The results indicated that a joint thickness of 20 mm preferred in wall production was more convenient. The strength of walls produced from durable material was found to be higher.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.323

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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