Freeze-Thaw Damage Assessment of Internally Insulated Historic Brick Masonry Walls Under Canada’s Future Climates
Bibliographic record
Abstract
Canada has taken steps to address climate change and protect heritage buildings by setting energy reduction targets and ensuring occupant comfort. Whereas internal insulation systems have emerged as a potential strategy to address these challenges, the use of such systems may also increase the risk to freeze-thaw (FT) damage of the exterior wall assembly and thereby lead to long-term deterioration of historic brick walls due to reduced drying capacity. Current standards provide general heritage preservation advice, but more specific technical guidance is needed to enhance thermal performance, ensure wall durability with interior insulation, and address climate change impacts on the masonry system. The information provided in this study is to contribute to the existing body of knowledge related to the long-term performance of historic masonry walls, by examining the FT damage of internally insulated historic brick masonry walls under a changing climate. In this study, recommendations are provided for optimal insulation selection to minimize freeze-thaw damage. \nTypically, a 30-year period is recommended to evaluate the long-term effects of climate change on building envelopes. However, an alternative approach is to select a single moisture reference year (MRY) that can accurately assess moisture stress over time, reducing the time and costs of simulations with multiple climate parameters. This study assessed the reliability of of presently used climate-based indices for selecting an MRY to evaluate the risk to FT damage in internally insulated brick walls. Finding the existing methods inadequate, the study proposed an alternative approach based on hygrothermal simulations. \nA parametric analysis was thereafter conducted to identify the key factors influencing FT damage in brick masonry walls. Simulations were conducted over a continuous 31-year period, as well as for each separate year, demonstrating no cumulative impact on annual FT cycles. The study determined that MRYs at the 93rd percentile severity could be employed for evaluating FT in retrofitting design decision-making. \nBy examining potential FT damage under different future climatic conditions and considering various factors, this research offers a decision-making process for internal insulation retrofit projects and proposes solutions when significant risk of FT deterioration is expected.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".