Investigating the performance of Superpave through the mechanistic–empirical (M-E) approach, field-evaluated performance, and laboratory test results: a case study on Ontario highways
Bibliographic record
Abstract
In North America, highway agencies have started using Superpave as it incorporates a performance-based asphalt binder specification and a mix design analysis system. However, in a pavement management system (PMS), the performance of the pavement structure significantly influences management decisions. In this regard, accurate prediction and evaluation of performance is a very important aspect. With this in mind, this study investigates the performance of Superpave through the mechanistic–empirical (M-E) approach, field-evaluated performance, and laboratory performance tests. It considers 15 sections of highways from Ontario. The investigation found that the international roughness index (IRI) and permanent deformation are overpredicted in the M-E approach compared with field observations. Additionally, to better understand the performance, the dynamic modulus of asphalt mixtures and binder rheological testing are also performed. The master curve developed for the surface mixtures suggests a lower level of fatigue resistance that justifies the bottom-up fatigue failure in the field-observed scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".