Temperature and Precipitation Sensitivity Analysis on Pavement Performance
Bibliographic record
Abstract
It is estimated that the average temperature in Canada will increase between 2°C and 5°C and precipitation will increase 0% to 10% over the next 45 years. These changes in climate will impact pavement performance and this paper attempts to predict the consequences of this performance change. Using Canadian data from the Long-Term Pavement Performance program, the Mechanistic–Empirical Pavement Design Guide (M-E PDG) version 1.0 is used to quantify the impact of climate change in the Canadian environment. In essence, two case studies representing Canadian conditions are presented. Specifically, how climate changes in precipitation and temperature affect the pavement performance indicators of International Roughness Index, longitudinal cracking, transverse cracking, alligator cracking, asphalt concrete deformation (rutting), and total rutting is assessed. Simulations were performed with combinations of 0%, –5%, +5%, +10% and +25% precipitation changes and 0°C, +1°C, +2°C, and +5°C temperature increases. Temperature increases have a negative impact on the pavement performance in the Canadian environment. Maintenance, reconstruction, and rehabilitation (MR&R) activities would be minimally affected with a 1°C increase in temperature. Based on the initial analysis, Canadian transportation agencies would likely not change MR&R activities until a 2°C or higher increase in temperature. The M-E PDG was not sensitive enough to distinguish between changes in precipitation or changes in transverse cracking. The CGC M2A2x and HadCM3B21 detailed climatic scenarios provide realistic prediction of the changes in pavement performance due to increases in temperature and precipitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".