Stochastic modelling of hygrothermal performance of highly insulated wood framed walls
Bibliographic record
Abstract
Recent years, the majority of building codes in North America require an energy efficient envelope to improve the building energy efficiency. There are different design strategies to achieve a higher insulation level of the wood framed building envelope, such as increasing the depth of stud cavity to accommodate thicker insulation or adding an exterior insulation while keeping the depth of stud cavity unchanged. However, the highly insulated walls may lead to a higher risk of moisture problems. The deep cavity walls will reduce the temperature of the wood sheathing, which may increase the potential for condensation and mold growth. The exterior insulated walls may result in a low drying capacity of the wood sheathing if the exterior insulation has a low vapour permeance. Although hygrothermal simulations have been widely used to investigate the moisture performance of wood framed walls, the uncertainty of the input parameters may lead to discrepancies between simulation results and real performance of the walls. This paper investigates the hygrothermal performance of highly insulated wood framed walls- deep cavity wall and exterior insulated wall using a stochastic simulation approach. The uncertainties of the input parameters including the material properties, air leakage and rain leakage rates are taken into account in stochastic modelling. The mold growth risks of the walls are evaluated based on the stochastic simulation results.
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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 | 0.000 | 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".