Effect of the future increases of precipitation on the long-term performance of roads
Bibliographic record
Abstract
The long-term performance of the road network of the province of Quebec (Canada) is strongly influenced by climatic conditions (Dore and Zubeck, 2008). Amongst other factors, high levels of saturation in soils and pavement materials are an important cause of pavement deterioration. According to climate change scenarios established by Ouranos (2012), the South of Quebec will undergo a monthly precipitation increase between -0.1% and 8.45% for a future horizon from 2010 to 2039. The purpose of this project is to quantify the effect of these expected precipitation increases on the mechanical behavior of road structures, materials and soils. Based on data collected on instrumented road sections, a relationship between precipitations increase and saturation level of pavement layers is proposed. In order to determine the existing relationship between mechanical properties and moisture content, the resilient modulus and permanent deformation behaviors for various moisture contents and four different subgrade soils were determined using triaxial tests, the later being validated using a small-scale heavy vehicle simulator. Using the precipitation increase scenario and the relationship developed between precipitation and pavement layers moisture content in Quebec, a damage analysis is performed to quantify the decrease of pavements service life caused by climate change. It is found that climate change, and more precisely the increase of precipitations expected in the Province of Quebec, will have a significant impact on pavement performance and that adapted pavement structures and materials, such as improved drainage, increased structural capacity or materials with reduced sensitivity to water, are possible options to reduce the loss of pavement service life associated with climate change.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".