Performance of Aged Asphalt Shingles and Development of Climate-Dependent Durability Index
Bibliographic record
Abstract
Over 80% of the North American residential (steep-slope) roofs are covered with asphalt shingles. As the water-shedding layer, shingles are exposed to a wide range of weather elements. Weather shocks can negatively impact the field performance of a shingle. Existing North American standards do not provide specifications to quantify properties subjected to the weather shock aging process, and no protocol exists to determine the long-term durability of a shingle. To demonstrate the effects of field and laboratory aging and to develop a framework for the durability of shingles, the National Research Council of Canada undertook a long-term experimental program to evaluate the performance of fiberglass shingles from four different sources. The experimental program studied three aging processes: as-purchased, lab-conditioned, and field-aged. Over 325 specimens were evaluated while focusing on three key properties: tear strength, tensile strength, and fastener pull-through resistance. Properties of the as-purchased samples were used as a baseline to quantify the effect of aging on the shingles’ durability. This paper includes the statistical significance of the measured data. Nevertheless, for simplicity, mean values are used to derive observations and conclusions. Based on those data, the field-aged shingles displayed a maximum reduction of over 50% in tear strength. The majority of the evaluated properties after aging no longer met the minimum requirements that are specified by the North American standards that are referenced in the building codes. Based on these limited data, a framework for a climate-dependent durability index has been proposed for developing a performance-based classification. Demonstration of the framework uses measured mean values. The ongoing experimental program will expand the database and revise, if needed, the presented classifications with data that will be statistically significant.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".