Temperature and Precipitation Sensitivity Analysis on Pavement Performance
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it