Influence of climate change on pavement design and materials in Canada
Bibliographic record
Abstract
Anthropogenic climate change is having and will continue to have adverse effects on Canadian weather. Trends over the last 50 years elevate the rapid increase in number of the extreme event, the variations in temperature and precipitation, etc. The severe climatic variations in Canada are in line with global climate changes occurring due to increased greenhouse gas concentrations in the atmosphere. Under the current CO₂ emission scenarios, scientists predict that climate trends will further intensify in the near future. It is well known that asphalt pavements are highly sensitive to climate factors. Hence, reviewing both pavement design and materials while accounting climate change is a vital step that can help decelerate pavement deterioration. This study aims to quantify the impact of climate change on pavement performance, including revising pavement design and materials. To achieve this, the temperature and precipitation data were extracted from ten statistically downscaled climate change models, which were gathered from the pacific Canada Climate database. Also, the pavement materials, traffic, and structural data were collected from the Long-term Pavement Performance (LTPP) database. All these data were used in the Pavement Mechanistic-Empirical (ME) software to determine the pavement performance for both baseline and future climate. Various adaptation strategies such as upgrading asphalt binder grade, increasing the thickness of asphalt concrete layer, increasing the base layer thickness, and using stabilized base layers were analyzed to mitigate the climate change impact and to extend the service life of the pavement. All of these adaptation strategies are based on climate change data and its effect on pavement performance. It is also evident that selecting a climate-appropriate asphalt binder is essential in ensuring the longevity of pavement surfaces. As the selection methodology depends on the pavement's temperature, several models can predict pavement temperatures based on recorded ambient air temperatures and other related factors. A commonality between the most predominant pavement temperature models is the geographical limitations to their application. As a result, widely used models such as the Long-Term Pavement Performance (LTPP) and Strategic Highway Research Program (SHRP) do not return accurate values for more Northern temperatures such as those observed in Canada. Thus, a new pavement temperature model was developed for Canadian climatic conditions to determine the appropriate asphalt binder grade for future climate. In addition, Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) were also carried out for all the alternatives to determine the CO₂ contributions to Canadian environment and changes in life cycle cost of Canadian pavement surfaces.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".