Improved assessment of wind-driven rain on building façade based on ISO standard with high-resolution on-site weather data
Bibliographic record
Abstract
Wind-Driven Rain (WDR) is one of the major moisture sources that cause building envelope failures. The quantity and spatial distribution of WDR are important considerations for durable building envelope designs and are essential boundary conditions for hygrothermal modelling. Within a comprehensive research program of quantifying WDR exposure of buildings and the effectiveness of overhang on reducing WDR wetting, a six-storey building located in Vancouver, Canada was instrumented for field WDR measurements. One of the challenges in field WDR measurements is the validity of on-site wind measurements. The accurate measurements of on-site wind conditions are essential for correlating WDR on façade with on-site weather conditions and for generating the spatial distribution correction factor required in the semi-empirical WDR models. This paper focuses on discussing the procedure necessary for proper on-site wind measurements for quantifying WDR on façade based on field and wind-tunnel measurements. The proper procedure to calculate the spatial distribution correction factor, namely wall factor according to ISO standard, and its impact on the accuracy of the ISO model is also discussed. The accuracy of ISO model can be significantly improved by using more detailed wall factors calculated based on high-resolution on-site wind and rain measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".