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Experimental and numerical characterization of the interface between concrete masonry block and mortar

2020· article· en· W3042972786 on OpenAlexaff
R. D. PASQUANTONIO, Guilherme Aris Parsekian, Fernando S. Fonseca, Nigel G. Shrive

Bibliographic record

VenueRevista IBRACON de Estruturas e Materiais · 2020
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Calgary
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMasonryMortarFinite element methodStructural engineeringGroutDiagonalInterface (matter)Ultimate tensile strengthEngineeringComputer scienceGeotechnical engineeringMaterials scienceMathematicsComposite materialGeometry

Abstract

fetched live from OpenAlex

Abstract Masonry is a construction system that has been used since the beginning of civilization and is still used throughout the world. The finite element method is a recent development that allows complex problems, including structural masonry problems, to be solved. A vast amount of literature exists on finite element modeling, using software such as ABAQUS, to represent experimental masonry models. Based on this established pattern, an experimental and analytical research program was designed and implemented. Thus, a set of tests was conducted to determine the compressive and tensile strengths of the masonry components, i.e., block, mortar, and grout. Bond wrench tests, diagonal tension tests, and horizontal joint shear tests were conducted to characterize the interface between the blocks and the mortar. A finite element model was then developed to represent the physical models and the general conclusion is that the finite element model was able to represent reasonably well the physical models.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.527

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 designBench or experimental
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

Citations11
Published2020
Admission routes1
Has abstractyes

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