Implications of Climate Variation in Flexible Airport Pavement Design and Performance
Bibliographic record
Abstract
Climate change impacts, especially in terms of rising temperatures and changes in precipitation patterns, have been widely noted in several regions in Canada during the past two decades. Such remarkable changes raise concerns about pavement serviceability, since both temperature and excess precipitation can ultimately affect the pavement materials properties. This paper focuses on two major factors, namely temperature and moisture level fluctuations. An increase in the maximum temperature can decrease asphalt concrete stiffness making the pavement more prone to rutting, shoving, and premature age hardening. On the other hand, high levels of saturation in the granular layers and subgrade can lead to pavement degradation through a reduction in the resilient modulus of these layers, increasing the rate of permanent deformations. However, accounting for realistic environmental factors in airport pavement design could be a challenge since most of the existing design methods do not systematically consider moisture and temperature variation during the service life of airfield pavements. Therefore, this paper proposes a methodology on how to efficiently account for temperature and moisture fluctuation by quantifying their effects on the performance of flexible airport pavements. To this end, the damage occurred over discrete short periods of time is estimated, considering the corresponding temperature and modulus at each stage. The cumulative damage is then calculated and compared with that of conventional design methods. The findings of this research indicate that it is crucial for airport pavement designers to consider realistic climatic factors to account for climate change implications in their airfield pavement design practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".