Assessment of Overlay Roughness in Long-Term Pavement Performance Test Sites: Canadian Case Study
Bibliographic record
Abstract
A study was conducted on asphalt pavement overlay performance in the Canadian environment. It investigated the impact of asphalt overlay thickness, climatic zone, and subgrade type on the progression of roughness as described by the international roughness index (IRI). Data from the Canadian Long-Term Pavement Performance (LTPP) test sites were analyzed. As a result of the investigation, pavement factors that significantly impact overlay performance in the Canadian environment were identified. Data collected over the first 13 years of study were used to show national and provincial roughness trends from 53 test sites. The IRI data were statistically summarized (mean, standard deviation) for each category by the age of the overlay section. With the summarized data, regression analysis was used to determine an equation that best describes the progression of roughness. Two-factor analysis of variance was used to determine if there were any significant differences within specific categories. The results of the regression analysis were compared with the Canadian Strategic Highway Research Project LTPP to confirm the validity of the roughness progression equations. Results show that overlay thickness and climatic zones significantly impact the roughness, while subgrade type has little influence on the IRI values. The roughness progression equations achieved squared correlation coefficients ( R2) between 0.93 and 0.39, demonstrating the accuracy of the model equations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".