Effect of Selected Moisture Reference Year on the Durability Assessment of Wall Assemblies under Future Climates
Bibliographic record
Abstract
Hygrothermal simulations are commonly used to evaluate the moisture damage risk of building envelopes over the long-term. For such assessment to be accurate, a proper selection of representative climate data is required. A common method is the selection of a moisture reference year from a set of available long-term climate data. For instance, the IRC-led research consortium MEWS (Moisture Management of Exterior Wall Systems) developped the Moisture Index (MI) approach, which consists of a wetting and a drying function. Therefore, the reference year selection would be based on the MI ranking. ASHRAE 160 is adopting a procedure named “the severity index” for the selection of moisture reference year. Combining climate loads and durability criteria, this method allows to select more “severe” weather years, thus providing a more representative ranking of the weather data. The objective of this paper is two-fold. First, to compare the selection of the moisture reference year based on two different approaches for both historical and future climate loads. Second, the effect of chosen representative years is evaluated and compared to long-term simulation periods (of 31-years) based on the durability of building assemblies. The methodology includes hygrothermal simulations of two different types of wall assemblies located in three different Canadian cities under a changing climate. In general, higher mold index values were obtained by the long-term simulation and MRYs using Isev. Comparing the results of different models under future climates, the three methods were in good agreement, except for a brick wall facing WDR in Ottawa and Vancouver. This might be due to the Isev correlations were developed based on a north-facing stucco wall. In addition, for a north-facing wall, an extremely low mold index was predicted for Vancouver, compared to WDR direction. Thus, considering a north-facing wall as a criterion for performance evaluation might misrepresent the reality in some locations. Hence, both WDR and North orientations should be considered. A further study will be carried out to investigate the performance evaluation of Isev method for different types of wall systems and orientations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".