Evaluation of subgrade and climatic zone influences on pavement performance in the Canadian Strategic Highway Program's (C-SHRP) Long-Term Pavement Performance (LTPP) study
Bibliographic record
Abstract
Sixty-five sections in 24 provincial test sites received pavement rehabilitation comprising of various thicknesses of asphalt overlays, as part of the Canadian Long-Term Pavement Performance (C-LTPP) study, which was initiated in 1989. This paper describes the impacts of the various alternative rehabilitation treatments on pavement performance in terms of roughness progression under comparative climatic, subgrade soil, and traffic loading conditions. Some findings from this study include (i) in wet, high-freeze zones, thinner overlays show a higher rate of roughness progression than thicker overlays, regardless of subgrade type; (ii) in dry, high-freeze zones, roughness progression for medium and thick overlays is relatively small; (iii) in wet, low-freeze zones, thinner overlays combined with fine subgrade soils show the highest rate of roughness progression; and (iv) traffic, in terms of equivalent single axle loads (ESALs), seemed to have a limited effect on all of the above; this was attributed largely to the fact that all of the traffic essentially fell into one level, where 200 000 ESALs per year was designated as the boundary between low and high traffic levels. The methodology developed in this paper provides valuable insight into how subgrade and climatic factors influence pavement performance and can be applied to performance trend analysis of other pavements with similar climatic, subgrade, and traffic loading conditions.Key words: subgrade type, climatic zones, pavement roughness, international roughness index (IRI), Long-Term Pavement Performance (LTPP).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".