Evaluation of Perpetual Pavement Design Philosophy for Three Traffic Volume Scenarios
Bibliographic record
Abstract
This paper evaluates the structural benefits of using perpetual asphalt pavement designs in comparison to the conventional pavement designs for three traffic levels. In addition, Life Cycle Cost Analysis (LCCA) was implemented to evaluate these various designs. Three scenarios were evaluated comparing the perpetual and conventional designs as applied on high, moderate and low traffic volume roads. The high traffic volume model represents a test section constructed on Highway 401 in Ontario, Canada. The moderate and low traffic volume scenarios were created based on traffic counts by the Ministry of Transportation of Ontario (MTO). Pavement designs for the moderate and low traffic volume scenarios are based on the American Association of State Highway and Transportation Officials (AASHTO) DARWin 3.1 methodology. The structural evaluation of the different pavement designs considered a 50-year analysis period using the Mechanistic Empirical Pavement Design Guide (MEPDG) model. The structural evaluation concluded that perpetual pavements have higher resistance to bottom up fatigue cracking, rutting and less deterioration rate of IRI compared to conventional pavements in the three scenarios. Finally, a 70-year LCCA was implemented on all three scenarios. The maintenance and rehabilitation programs used in LCCA were designed based on the MEPDG results and the typical MTO practices. The LCCA showed that perpetual pavement designs are the cost-effective alternative in high and moderate traffic volume scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".