Implementation of the Mechanistic-Empirical Design Guide for Canadian Municipal Pavements
Bibliographic record
Abstract
The City of Toronto maintains a network of about 5,200 centreline-kilometres of roads and over 250 centreline-kilometres of laneways. This pavement infrastructure has been constructed, maintained, and enhanced over more than 100 years. Over the past several decades, fuelled by research and other technological advancements in the pavement engineering field, a gradual shift is being observed in terms of how pavements and pavement materials are designed, tested, evaluated, constructed, and managed. As part of this effort, pavement design is transitioning from an empirical to a mechanistic-empirical realm. The U.S. National Cooperative Highway Research Program (NCHRP) Project 1-37A was issued to develop a new pavement design guide based on mechanistic-empirical principals. This paper describes the implementation of the Mechanistic-Empirical Pavement Design Guide (ME PDG) procedures for a municipal roadway network in the City of Toronto, Canada in accordance with the principles set out in the ME PDG. The results show that the pavement performance is adequately modeled for fatigue and thermal cracking, but that the roughness and deformation models do not apply well in the municipal setting.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".