A case study on developing asphalt mix performance grading (Mix-PG) system in Ontario
Bibliographic record
Abstract
Abstract Road infrastructure plays a crucial role in facilitating the movement of goods and people, promoting global economic growth, trade, and connectivity. Achieving sustainability and resiliency within road systems extends their lifespan, stabilizes economies, enhances climate adaptability, and reduces both maintenance costs and environmental impact. With the increasing use of recycled materials and chemical additives in asphalt mixes, relying only on asphalt binder Performance Grading (PG) is insufficient for predicting field performance of asphalt pavements and material resilience under various climatic projections and severe weather events. Therefore, this study introduces the concept of asphalt mixture PG system (Mix-PG system) for evaluating asphalt materials resilience. The proposed Mix-PG system is demonstrated using the following three laboratory tests: (1) the Disc-Shaped Compact Tension (DC(T)) test assessed low-temperature cracking resistance, establishing a minimum threshold for fracture energy to determine the continuous low-temperature PG, (2) the Hamburg Wheel Tracking (HWT) test measured rutting resistance, with a maximum threshold for creep slope defining the continuous high-temperature PG, and (3) the Illinois Flexibility Index Test (I-FIT) evaluated intermediate-temperature cracking resistance, ensuring mixes meet a minimum Flexibility Index (FI) threshold value. This research revealed that meeting a single threshold value of a laboratory test, at a single temperature, may not result in a comprehensive evaluation of mix performance and durability at various climatic projections and material resilience under severe weather events. However, a mix PG assessment diagram—integrating low- and high-temperature PGs and FI values—offers a comprehensive framework for assessing asphalt materials’ resilience for major decisions and high-profile projects based on expected climate performance and projections. The Mix-PG system is not intended for routine mix design or quality control/assurance. Instead, it is proposed for critical decision-making scenarios—such as evaluating alternative mix designs or incorporating innovative materials—particularly in high-profile projects like major highways, high-traffic intersections, or projects where climatic resilience is a key design objective. Additionally, the proposed framework may be applied at the network level to assess typical climatic resilience using common mixture types, thereby helping to identify potentially vulnerable areas under various climate change scenarios. Ultimately, this approach may be used in supporting the development of resilient and sustainable road infrastructure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".