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Record W2021388208 · doi:10.1115/imece2012-85746

Numerical Investigation of the Effects of Different Overhang Configurations on the Wind-Driven Rain Wetting of Building Facades

2012· article· en· W2021388208 on OpenAlexafffund
David Naylor, Hua Ge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsConcordia UniversityToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFacadeBuilding envelopeEnvelope (radar)Wind speedEnvironmental scienceMeteorologyDurabilityLow-riseRADIUSWind engineeringStructural engineeringComputer scienceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

The distribution of wind-driven rain loads on building facades is of high importance in evaluating the hygrothermal performance and durability of building envelopes. Wind-driven rain is known to be a major source of moisture loads on building facades and is responsible for numerous cases of facade failure. One of the classical solutions for preventing the building envelope from being extensively exposed to such loads is the use of overhangs. In this work, the effects of different overhang lengths on the distribution of wind-driven rain on building facades are investigated by numerical methods. To check validity, the results are compared to existing data reported in the literature. Calculated values of catch ratio at two sample positions on the front facade of a simple cubic building are in good agreement with previously published results. The protective effect of overhangs is shown by calculating catch ratio values for cases where rectangular overhangs were added to the cubic building. Results suggest that the introduction of the overhang can significantly change both the amount and the pattern of the wind-driven rain wetting of the facade. This change may, in certain points, be unfavourable.

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 score0.178
Threshold uncertainty score0.125

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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