Hygrothermal performance assessment of split insulated cork wall assemblies under various moisture load conditions
Bibliographic record
Abstract
Abstract Every year along with the implementation of energy-saving, energy conservation and other green energy initiatives the demand for effective, but sustainable, renewable insulation materials in the construction industry increases. It is worth mentioning that the selection of insulation materials nowadays is not limited to its cost and technical characteristics only, but health-related aspects and carbon footprint are also taken into consideration. However, there are not many insulation materials that have competitive technical characteristics, are sustainable, renewable and do not pose risk for health. Cork and cork-based materials like insulation cork boards (ICB) are of these types of materials which have unusual combination of material properties, have low to negative carbon footprint and have low to almost zero negative impact on the ecology and human health during the whole life cycle and later on. That is why with the increasing demand for sustainable, renewable and ecological materials the interest toward cork in North America is expected to increase. However, there are not so many researches performed on lightweight wall assemblies common in North America with cork insulation applications. In this paper, the hygrothermal performance of natural cork insulation used in split wall assemblies is compared against similar, commonly used mineral wool and expanded polystyrene (EPS) wall assemblies for three different Canadian climates, using WUFI hygrothermal analysis computer simulation tool. The relative performance of seven wall assemblies, with various combinations of insulation type and vapor control strategies, exposed to different moisture loads including elevated indoor humidity, air leakage and rain penetration are presented. The simulation results suggest that, in general, assemblies with cork have a slight advantage in performance against the EPS assemblies, especially when the amount of moisture affecting the assemblies is high. In most cases, assemblies with mineral wool perform better than that of with cork and EPS insulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".