Use of Asphalt Pavement Analyzer Testing for Evaluating Premium Surfacing Asphalt Mixtures for Urban Roadways
Bibliographic record
Abstract
This paper describes how urban roadway pavement rutting, particularly at signalized intersections, is a significant issue and challenge for those responsible for municipal pavement infrastructures. Agencies have, in recent years, been proactive in trying new strategies for dealing with this problem. These strategies have included premium hot mix asphalt (HMA) surfacing materials including the use of polymer modified or performance grade asphalt binder, stone matrix asphalt (SMA), Superpave mix types, sulfur extended asphalt modifier, and asphalt rubber mixes. The Cities of Calgary and Lethbridge have recently installed various types of pavement surfacing materials with the objective of assessing performance under in-service conditions. The limited experience with these types of hot mix asphalt materials has made it necessary for the municipal agencies to adopt available methods to provide some indication of their performance prior to their widespread use in construction. The Asphalt Pavement Analyzer (APA) has been shown to be a useful tool for the assessment of the permanent deformation (rutting) resistance of HMA materials. This paper describes how APA testing was used for the assessment of HMA materials within the context of projects undertaken by the Cities of Lethbridge and Calgary, in 2003 and 2004. In some cases the APA testing was used to validate and/or optimize HMA mixture designs. This means of accelerated testing was also used to evaluate the rut resistance performance of constructed pavements, with the objective of assessing the different materials and construction strategies. Guidance regarding the use of APA results, including benefits as well as limitations, is also provided.
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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 | 0.003 | 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".