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Record W4388657573 · doi:10.1061/9780784485163.021

Ensuring the Durability of Masonry Cavity Wall Construction in a Changing Canadian Climate: Overview of a Transformative Industry-Driven Project at McGill University

2023· article· en· W4388657573 on OpenAlexaffabout
Lindsay Saad, Alexander Menun, Justin Di Costa, Moustafa El-Assaly, Mohamed Hosny, Adrien Sparling, Daniele Malomo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsTransformative learningMasonryDurabilityArchitectural engineeringEngineeringConstruction engineeringCivil engineeringComputer scienceSociology

Abstract

fetched live from OpenAlex

In masonry cavity walls, the outer clay brick veneer experiences moisture/temperature-induced strains throughout its service life, while the concrete block backup, protected from temperature/moisture fluctuations by the building envelope, is prone to drying shrinkage. These long-term volume fluctuations, which may become more severe with climate change, cause differential movements between the veneer and backup and also cause distress in cavity walls and affect their durability if not properly accounted for in design. In Canada, masonry cavity wall designs rely on outdated experimental data that does not consider the interaction between mortar and masonry, while lacking substantial evidence from long-term field tests on modern structures. In this paper, preliminary results from a novel 2-step testing method for drying shrinkage on concrete masonry prisms are discussed. Future research includes moisture expansion tests on clay brick prisms, as well as mechanical tests on tie-masonry assemblies and wall-shelf angle assemblies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.243
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2023
Admission routes2
Has abstractyes

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