PERFORMANCE OF ASPHALT OVERLAYS IN THE CANADIAN STRATEGIC HIGHWAY PROGRAM'S LTPP STUDY
Bibliographic record
Abstract
The Canadian Long Term Pavement Performance (C-LTPP) study, initiated in 1989, involves 65 sections in the 24 provincial sites that received various thicknesses of asphalt overlays. This paper describes the impacts of these overlays on pavement roughness progression under comparative traffic loading, climate, and subgrade soil conditions. Progression of roughness for thin overlays (30-60 mm) is significantly higher on a national basis than for medium (60-100 mm) and thick (100-185 mm) overlays. Factor effects, including climatic zone, subgrade type and traffic level were also evaluated. Some findings are that: (a) in wet, high freeze zones, thinner overlays show a higher rate of roughness progression than thicker overlays, regardless of subgrade type; (b) in dry, high freeze zones, roughness progression for medium and thick overlays is relatively small; (c) in wet, low-freeze zones, thinner overlays combined with a fine subgrade show the highest rate of roughness progression, (d) traffic, in terms of equivalent single axle loads (ESALs) seemed to have a limited effect for all the above; this was attributed largely to all the traffic essentially falling into one level. In conclusion, the C-LTPP experiment has provided valuable information on roughness trends after only eight years of observations. The methodology developed in this study for pavement roughness evaluation can be applied to performance trends analysis of other measured LTPP data. (a) For the covering entry of this conference, please see ITRD abstract no. E202467.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".