Evaluating the potential of freeze-thaw damage in internally insulated masonry under climate change using different models
Bibliographic record
Abstract
To mitigate the effects of climate change, higher insulation levels in buildings are mandated by the National Energy Code for Buildings. However, increased insulation levels within building envelopes may lead to a greater risk of moisture problems. With a changing climate, higher rainfall intensity, stronger winds and more storms are expected, which may increase wind-driven rain loads on façade and risks for rain penetration damages of building envelopes. This paper aims to present results of the effects of climate change on the freeze-thaw damage risk of internally insulated brick masonry walls of buildings in different Canadian cities, using different freeze-thaw models. Freeze-thaw damage was evaluated using different freeze-thaw models. Simulations were performed using DELPHIN 5.9.4. Results showed potential risk to freeze-thaw in Montreal and Vancouver after retrofit. Under climate change, Winnipeg has the lowest risk to frost damage, though damage functions showed an increase in the level of severity. Comparing the results of different models under a changing climate, the damage functions seemed in a good agreement for most of the cases, except for the Indicative Freeze-Thaw Cycles (IFTC) evaluated in St-Johns. This model counts the number of freeze-thaw cycles based on short duration of freezing and thawing and therefore does not consider longer freeze-thaw period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".