Evaluation of climate impacts on jointed plain concrete pavement structures
Bibliographic record
Abstract
Canadian pavement infrastructures, now more than ever, face risks associated with the potential impacts of climate and extreme weather events. Canada has experienced and continues to experience a number of changes to environmental variables affecting the performance of pavements, including temperature, precipitation, sea level rise, flooding, and extreme weather events. Therefore, road agencies and public are increasingly concerned with climate resiliency of pavement infrastructures which were not intended to accommodate intense environmental conditions due to climate change. While much has been written about the general behavior of flexible pavements in response to climate change, yet there has been relatively scant investigation of the rigid pavement climate resiliency and sustainability. This paper primarily focuses on the vulnerability and long-term performance of Jointed Plain Concrete Pavement (JPCP) Structures from Mechanistic-Empirical Pavement Design Guide (MEPDG) perspective. In this paper, climatic data obtained from the latest Canadian Regional Climate Model (CanRCM4) were used. Simulation results from incorporating the projected climate data into AASHTOWare Pavement ME Design software showed that the magnitude of impacts and the degree of vulnerability arising from climate change was inconsistent between different performance indicators. Also, sensitivity analysis of the MEPDG distress models to multiple climatic factors revealed different trends of variation depending on climate variable.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".