The Impact of Variability in Moisture Storage Properties of Wood Based Sheathing on Enclosure Durability Due to Temperature and Weather-Based Ageing
Bibliographic record
Abstract
<p>A key component of building performance with respect to durability and energy efficiency is at the meso-level, i.e., the wall assembly and components. Performance of these assemblies can be determined through in-situ experimental work (such as building and monitoring a test hut), or by hygrothermal modelling. Modelling is highly dependent on inputs including material data, thermodynamic equations, and weather data. This research examines material moisture storage, which is not well measured in comparison to thermal and vapour resistance. A parametric study was completed to determine the effects of variable moisture storage properties on mould growth risk based on commercial hygrothermal modelling software. These results demonstrated that variations in moisture storage via the sorption isotherm of as little as 10% can increase mould growth risk from a low to high category. Plywood and OSB samples were then measured during the experimental phase. Accelerated ageing was completed on some samples, and samples were measured under varying temperatures. The results of the measurements demonstrate that age may not have a significant effect on moisture storage, but temperature variations of approximately 15°C can result in sorption isotherm variation of, on average, 10%. Finally, the measured results were input into hygrothermal modelling software to compare mould growth risk using sorption isotherms that were adjusted for age and temperature. These results were also compared to in-situ test hut data from previous work. The use of sorption isotherms adjusted for age and temperature yielded lower mould index values of between 30% (exterior face of sheathing) and 60% (interior face of sheathing), demonstrating that using a single sorption isotherm in hygrothermal modelling of plywood and OSB wood frame walls over-predicted the mould growth risk.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".