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Effects of climate change on the moisture performance and durability of brick veneer walls of wood frame construction in Canada

2021· article· en· W4200619716 on OpenAlexaffabout
Maurice Defo, Michael Lacasse, L Wang

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDurabilityRoofMoistureBrickEnvironmental scienceVeneerDrainageMeteorologyGeographyEngineeringCivil engineeringMaterials scienceEcologyComposite material

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to assess the potential effects of climate change on the moisture performance and durability of red matt clay brick veneer walls of wood frame construction on the basis of results derived from hygrothermal simulations. One-dimensional simulations were run using DELPHIN 5.9 for selected moisture reference years of the 15 realizations of modelled historical (H: 1986-2016) and future (F: 2062-2092) climate data of 12 Canadian cities. The mold growth index at the outer layer of the OSB sheathing panel was used to compare the moisture performance under H and F periods. Results for the base design that meet the minimum requirements of the National Building Code of Canada showed that cities within the interior of the country, characterized by a low annual rainfall, are less likely to develop significant mold growth under H and F periods, whereas cities in coastal areas, characterized by high annual rainfall, present a heightened risk to mold growth under both H and F periods. For cities located on the west coast, a possible solution could be to use a 38-mm ventilated drainage cavity as this measure would help dissipate moisture from within the cavity. On the east coast, apart from using a 38-mm ventilated drainage cavity, other measures aiming at reducing the wind-driven rain deposition (i.e., increasing overhang ratio or the height of the roof) could be introduced. However, the feasibility of such measures needs to be considered in respect to whether these are to be implemented as part of a new building or retrofit of an existing one.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.179
Teacher spread0.169 · 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 designObservational
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

Citations4
Published2021
Admission routes2
Has abstractyes

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