Stochastic Simulation of Mould Growth Performance of Wood-Frame Building Envelopes under Climate Change: Risk Assessment and Error Estimation
Bibliographic record
Abstract
Previous studies have shown that the effects of climate change on building structures will increase the mould growth risk of the wood-frame building envelope in many circumstances. This risk can be controlled by wind-driven rain deflection, improving water tightness of the exterior facade, and improving cladding ventilation. However, the effectiveness of these risk mitigation strategies are subject to various uncertainties, such as the uncertainties of wall component properties and micro-climatic conditions. The objective of this paper is to apply stochastic hygrothermal simulation to evaluate the mould growth risk of a brick veneer-clad wood-frame wall with a drainage cavity under historical and future climatic conditions of Ottawa, a Canadian city located in a cold climate zone. An extensive literature review was conducted to quantify the range of stochastic variables including rain deposition factor, rain leakage moisture source, cladding ventilation rate and material properties of brick. The randomised Sobol sequence-based sampling method, one of the Randomized Quasi-Monte Carlo (RQMC) methods, was applied for risk assessment and error estimation. It was found that, under the climatic condition of Ottawa, limiting the amount of wind-driven rain to which walls are subjected is a more robust mitigation measure than improving cladding ventilation in controlling mould growth risk, the improving of water tightness of exterior façade is not as robust as wind-driven rain deflection and cladding ventilation, however, the reduction of rainwater penetration can reduce the mould growth risk at different levels of rain deposition factor and cladding ventilation rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".