Impact of Climate Change on Thermal Behavior of Pavement Structures in Ontario
Bibliographic record
Abstract
In recent years, numerous studies have highlighted that the climate across the world is changing rapidly due to increased Green House Gas (GHG) emissions. The Intergovernmental Panel on Climate Change (IPCC) has reported that ambient temperatures across Canada are rising twice than the rest of the world. In light of climate change, it is vital to adapt our best practices in pavement material selection and road weight restrictions to avoid potential disruption. Traditionally, asphalt binder selection based on the Superior Performing Asphalt Pavements (Superpave) Performance Grade Asphalt Concrete (PGAC) system relies on historic climatic conditions in relation to the expected in-service temperature range of the flexible pavement. Moreover, in Canada, the Spring Load Restriction (SLR) periods are imposed on the basis of subsurface temperature data obtained from Road Weather Information System (RWIS) and Spring Load Adjustment (SLA) stations in conjunction with visual observations. In view of climate change, it is crucial to investigate the extent to which pavement surface and subsurface temperatures will be affected by ambient conditions in the future. This is to assess the relative impact on appropriate PGAC selection and appropriate SLR recommendations for more durable and resilient pavement structures. In this study, regression models were developed to determine the relationships between asphalt pavement surface temperature and ambient weather data from various weather stations within the Ontario Ministry of Transportations RWIS. Moreover, this study also involves the investigation of climate change effect on SLR periods using future climate projections. Regression models were developed to determine the relationships between freeze/thaw depths and climate indices using data from existing SLA and RWIS stations within Ontario. Firstly, the relative impact of climate change on pavement surface and subsurface temperature extremes were estimated for different Representative Concentration Pathways (RCPs) using the regression models. After that, appropriate PGAC selection and SLR recommendations to meet projected pavement temperatures were assessed. It was anticipated that in the future, climate change could potentially cause changes to asphalt binder grades and changes in SLR periods across the Province of Ontario depending on the severity of the projected warming due to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".