Application of modeling tool to assess moisture and thermal performance of retrofitted wall assemblies
Bibliographic record
Abstract
As the stock of buildings in Canada ages, it is expected that there will be an increase in building envelope rehabilitation work. Such activities represent an ideal opportunity to add insulation and reduce air leakage to improve energy efficiency and building envelope durability. However, there is very little information available on how to assess the moisture and thermal (i.e. energy) performance of retrofitted building envelope assemblies and select the optimum retrofit options that will maximize the energy efficiency without compromising the long-term moisture performance of the retrofitted building envelopes. This paper depicts selected results from a study that has used a two-dimensional hygrothermal simulation tool, hygIRC-2D, to assess thermal and moisture performance of retrofitted masonry walls used in high-rise construction. The performance analyses of three basic (i.e. base case) masonry wall systems (Brick Veneer - Steel Stud, Brick Veneer - Concrete Masonry, and Precast Concrete Panels - Steel Stud) with four retrofit options, located in the National Capital Region (Ottawa-Gatineau) of Canada, are presented in this paper. The results from the simulations indicate that hygrothermal simulation tools can be used to evaluate the thermal and moisture performance of various wall systems and associated retrofit options. Simulations results also indicate that with specific retrofit options the energy performance of the wall system can be improved significantly without compromising the moisture response of the wall by adding insulation and reducing air-leakage in the wall assembly. However, heat or energy loss through the wall system is directly proportional to the air-leakage characteristics of the wall system. In general, based on the results presented in this paper, it can be concluded that use of a hygrothermal simulation tool can help to identify potentially problematic retrofit strategies while more promising measures can be advanced for additional assessment through full-scale laboratory testing or field demonstration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".