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Record W3136312740 · doi:10.3390/buildings11030117

Assessing the Moisture Load in a Vinyl-Clad Wall Assembly through Watertightness Tests

2021· article· en· W3136312740 on OpenAlexafffundabout
Zhe Xiao, Michael Lacasse, Maurice Defo, Elena Dragomirescu

Bibliographic record

VenueBuildings · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of OttawaNational Research Council Canada
FundersInfrastructure Canada
KeywordsMoistureEnvironmental scienceWind speedASHRAE 90.1Intensity (physics)Water contentStructural engineeringClimate zonesGeotechnical engineeringEngineeringMeteorologyMaterials scienceComposite materialGeology

Abstract

fetched live from OpenAlex

The moisture load in wall assemblies is typically considered as 1% of the Wind Driving Rain (WDR) load that is deposited on the surface of wall assemblies as specified in the ASHRAE-160 standard whereas this ratio has been shown to be inaccurate as compared to results derived from several watertightness tests. Accurate assessment of moisture loads arising from WDR can be obtained through the watertightness test during which different levels of WDR intensities and Driving Rain Wind Pressures (DRWPs) are applied to a test specimen and water that penetrates wall assembly can thus be quantified. Although many previous studies have included watertightness tests, only a few of these have attempted to correlate the moisture loads to WDR conditions as may occur in specific locations within a country. To improve the assessment of moisture loads for a vinyl-clad wall assembly, a wall test specimen was tested following a test protocol based on local climate data using National Research Council of Canada’s Dynamic Wind and Wall Testing Facility (DWTF). The use of this test protocol permitted quantifying the moisture load in the vinyl wall assembly when subjected to several different simulated WDR conditions. The moisture load was formulated as a function of the WDR intensity and DRWP which thereafter allowed evaluating the moisture load based on a given climate’s hourly rainfall intensity and wind velocity. Such work is particularly relevant considering that the intensity, duration and frequency of WDR events across Canada will in some regions increase due to the effects of climate change.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.431

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 head0.017
GPT teacher head0.275
Teacher spread0.257 · 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 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

Citations1
Published2021
Admission routes3
Has abstractyes

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