Analysis of Influences on As-built Pavement Roughness in Asphalt Overlays
Bibliographic record
Abstract
Pavement roughness immediately after construction is a key measure of quality. The use of smoothness specifications requires an understanding of the influences on as-built roughness for both transportation agencies and contractors. This paper uses data from the Long-Term Pavement Performance (LTPP) program to examine four factors to determine their effects on the as-built roughness of a pavement. These factors include the extent of surface preparation prior to resurfacing, overlay thickness, type of overlay material and pavement roughness prior to resurfacing. Various statistical procedures including paired data analyses, regression analyses and a repeated measures analysis are performed to investigate these effects and any interactive effects. The extent of surface preparation, overlay thickness and pavement roughness prior to resurfacing are determined to have statistically significant effect (at a 95 % significance level) on the as-built roughness of a pavement either directly or interactively with another variable. The overlay mix type is determined not to have an influence on as-built pavement roughness. Data from the Canadian Long-Term Pavement Performance (C-LTPP) program is used to validate the results for overlay thickness and pavement roughness prior to resurfacing. A series of prediction equations are also developed to allow for estimating the as-built roughness of a pavement under various conditions. Pavement designers, construction engineers and contractors should understand the effects that influence the as-built roughness of a pavement so that they can maximize their designs, smoothness specifications and/or bidding of contracts with smoothness specifications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".