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Record W3167728654 · doi:10.11575/prism/38877

Bond Strength of Adhered Manufactured Thin Stone/Brick Masonry Veneer Units to Setting Bed Mortar at Different Temperatures and Different Cycles of Freeze-Thaw

2021· dissertation· en· W3167728654 on OpenAlexaboutno aff
Samira Rizaee

Bibliographic record

VenuePRISM (University of Calgary) · 2021
Typedissertation
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsVeneerMasonryMasonry veneerMortarBond strengthMaterials scienceBrickGeotechnical engineeringBrick and mortarComposite materialStructural engineeringEngineeringForensic engineeringGeologyComputer scienceAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

Close to no study has been conducted to identify the possible influential physical and mechanical characteristics of adhered thin masonry units and mortars on the bond strength in adhered thin masonry veneer (ATMV) applications. There have been numerous cases of bond failures in ATMV applications probably due to lack of knowledge and the lack of any standards for the design and installation of ATMV. In the Canadian climate, exposure to extreme weather conditions like very low temperatures and freeze-thaw cycles are common and may be a cause of bond deterioration and failures. Therefore, it is important to study the effects of exposure to extreme weather conditions on the bond strength. This research first measures and evaluates the physical and mechanical properties of thin masonry units and mortars. Then shear and tensile bond strengths are studied considering different age and environmental factors. At least three different ages, two different curing temperatures, three testing temperatures and four cycles of freeze-thaw were considered. Subsequently, any possible relationship between these characteristics and bond strengths were evaluated and proper practices were recommended.

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 categoriesMeta-epidemiology (narrow)
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 score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

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