Drainage of infiltrated rainwater in wall assemblies: Test method, experimental quantification, and recommendations
Bibliographic record
Abstract
Drainage reduces the amount of water able to infiltrate toward the interior of wall assemblies. However, a portion of the infiltrated water remains in the assembly after drainage has occurred. The degree to which this retained portion of water affects the durability of the wall assembly can be evaluated by means of hygrothermal simulations. However, the number of studies reporting information on the retention percentage that can be applied as input for hygrothermal simulations and on the drainage performance of wall assemblies is, in general, quite limited. Therefore, an experimental study was developed, to assess governing test methods to evaluate drainage characteristics and to quantify retention of water in wall test specimens having various cavity widths and incorporating different drainage materials. It was concluded that apart from the absolute amount of retained water, the lateral spreading of water in the cavity and the overall wetted area, should also be considered, thereby resulting in reporting the retained amount relative to the wetted area. The latter values provide more detailed information on the behavior of water in the cavity. Additionally, it was concluded that a clear cavity of 1 mm can drain water more efficiently than a cavity of 10 mm. As well, the surface texture of drainage materials affected the spreading and retention of water within the cavity and the use of a drainage mat in the cavity resulted in an increased relative retention but a reduced lateral spreading of the water.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".