Parametric Study of Moisture and Heat Transfer in a New Rain-Screen Stucco Wall
Bibliographic record
Abstract
The cost of repairs to leaky condominiums in southwestern British Columbia (BC) has been estimated at one billion dollars. Stakeholders have been actively trying to address the problem. Providing a drainage cavity (rain-screen) in wall claddings is emerging as the promising solution to offer a second line of defense against moisture ingress and is currently advocated by municipal authorities, designers and manufacturers of building envelope systems. However, it is not yet clear which assembly of materials will provide an optimal solution or what size of drainage cavity should be used. Designers do not agree on whether the cavity should be pressure equalized, pressure moderated or vented and to what extent. The effects that the cavity will have on the drying and energy efficiency of walls also remain open questions. Since stucco has been a major cladding material in BC, extensive research to design an effective drainage cavity stucco cladding system was carried out. A detailed computer study using the “MOIST 3.0” computer model was conducted to analyze the moisture and heat transfer in a new drainage cavity system versus a conventional stucco wall. The ASHRAE WYEC hourly weather data for the city of Vancouver along with the variable indoor relative humidity model were used as input. Results show superior characteristics of the proposed drainage cavity cladding system. Several residential projects, including multifamily buildings, have been constructed using the new rain-screen system, which is exhibiting good performance.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".