Assessing the Methodology of Freeze-Thaw Degradation in Varying Ages of Brick Masonry Through Frost Dilatometry
Bibliographic record
Abstract
<p>The need for improving the energy efficiency of existing buildings is increasing as the threat of climate change also increases. Load bearing masonry buildings are predominant in major cities such as Toronto, Ottawa and Edmonton. The material was popular in the nineteenth century due to the durability and low risk of fire. However, due to the historic significance of many of these buildings, the addition of insulation occurs on the interior of the masonry wall when retrofitting. This increases the risk of freeze-thaw damage because the ability of the wall to dry out is reduced. </p> <p>Brick samples are subject to repeated freeze-thaw cycles at specific moisture content levels to determine the exact point at which dilation occurs, known as the critical degree of saturation, Scrit. It appears that the existing methodology has conservative minimum freezing temperatures, to ensure that the majority of pores freeze. However, it is unclear if freezing to low temperatures (-15°C) is necessary to produce viable results. </p> <p>Both modern and heritage brick samples were used to compare the results of freeze/thaw cycles using different test variables. As previous literature has stated that significant damage occurs between -4°C and -10°C, prompting three tests with minimum freezing temperatures of - 2°C, -6°C and -15°C. An additional three freezing rates were tested, including: -5°C hr, -12°C /hr and -21 °C /hr to understand whether the freezing rate could impact the resulting Scrit in both modern and heritage bricks. It appears that the minimum freezing temperature of -15°C is the preferred freezing temperature to use, as it allows for the majority of water inside the bricks to freeze, causing damage throughout the freeze/thaw cycles. However, it is unclear whether the freezing rate has any impact on the Scrit results, as the results obtained from the freeze/thaw cycles are unreliable. </p>
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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 | 0.002 | 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) | 0.001 | 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 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".