Effect of rain leakage on hygrothermal performance of highly insulated wood-framed walls: a stochastic approach
Bibliographic record
Abstract
Wind-driven rain is one of the most important moisture sources that may lead to moisture damage risks of building envelopes, particularly when the rainwater unintentionally infiltrates into the envelope assembly through defects of the wall components. This paper proposes a stochastic modelling approach to evaluate the impact of rain infiltration on hygrothermal performance of highly insulated wood-framed walls, including an I-joist deep cavity wall, two exterior insulated walls, and a conventional 2×6 stud wall as the baseline wall. The stochastic hygrothermal models of the walls are created based on the uncertainties of material properties and rain deposition factor under different scenarios with rain infiltration deposited at different locations of the wall assembly. The stochastic simulation results show that the rainwater deposited on a water resistive barrier does not cause any moisture damage risk, but there is a significant risk when the rainwater is directly deposited on the exterior or interior surface of wood sheathing, and the interior deposition has the highest risk.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".