Towards Comparing Critical Degree of Moisture Saturation (SCrit) in Historic Brick Samples for Different Freezing Rates and Minimum Temperatures
Bibliographic record
Abstract
<p>As more historical buildings are being retrofitted, it is becoming more important to have an efficient procedure to test historic masonry’s capacity to withstand freeze-thaw cycling. Frost dilatometry testing focuses on establishing a critical degree of saturation (S<sub>crit</sub>) at which masonry will fail if subjected to freeze-thaw cycles. Past studies in frost dilatometry attempt to reduce the total laboratory testing time by modifying different parameters, such as the freezing rate. This MRP focuses on the relationship between S<sub>crit</sub> and freezing temperature, and S<sub>crit</sub> and freezing rate. Brick samples from three historic masonry sites in Toronto were saturated at varying moisture contents between 60% and 100%. Three freezing temperatures, -2C, -6C and -15C, and three freezing rates, -5C/hr, -12C/hr and -21o C/hr, were tested. The samples were initially subjected to 12 cycles; however, upon discovering that there was little correlation in the data, an additional 30-cycle frost dilatometry tests were performed. As a result of this study, it was found that there is repeatable correlation between Scrit and freezing rate, with lower freezing rates resulting in higher S<sub>crit</sub> values. While freezing temperatures also affect S<sub>crit</sub> values, a definitive correlation between S<sub>crit</sub> and freezing temperature was not established as part of this work. However, it was noted that the lowest freezing temperature (-15C) produced data that was more precise, and therefore it was generally easier to establish a linear trend to determine the S<sub>crit </sub>values at the lowest freezing temperature.</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 | 0.000 | 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) | 0.000 | 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".