A brief compendium of water entry results derived from laboratory tests of various types of wall assemblies
Bibliographic record
Abstract
There is an increase in the use of hygrothermal models to complete the performance evaluation of walls assemblies, either in respect to design of new assembles or the retrofit of existing wall assemblies. To this end there are guides available in which is provided information on moisture loads to wall assemblies. This includes, for example, Criteria for Moisture-Control Design Analysis in Buildings given in ASHRAE 160, Assessment of moisture transfer by numerical simulation provided in EN 15026, and NRC’s “Guidelines for Design for Durability of the Building Envelope”. The designer of a new assembly or evaluator of an existing wall is tasked with having to determine what moisture loads to apply to the wall and where to apply this load within the assembly. Typically there is little or no information that is readily available regarding moisture loads to walls and thus the suggested hourly moisture load, as given in ASHRAE 160, is 1% by weight of the total driving rain load to the wall (i.e. kg/m2-hr). In this paper, a brief compendium of water entry test results derived from laboratory tests of various types of wall assemblies is provided from which estimates of moisture loads to different types of wall can be developed. Water entry test results are given of wood frame walls typically used in housing, but also metal-glass curtain walls and other commercial wall assemblies, where possible, in terms of driving loads to the wall.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.021 |
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".