Skid Resistance of Asphalt Pavements with Different Surfacing Materials in the City of Calgary (Poster)
Bibliographic record
Abstract
Pavement skid resistance or friction is one of the main safety considerations in pavement design and construction. Although safety factors such as pavement friction should be considered in all pavement engineering projects, most of pavement projects are designed and constructed only based on life cycle cost analysis which does not take into account the indirect costs of accidents due to lack of friction. The City of Calgary is committed to improve the safety of its road network by monitoring pavement friction of its various surfacing materials. The City of Calgary conducted a study to evaluate its different paving materials. Several pavement sections in the City which were paved in 2010 and 2011 were tested in the Fall of 2011 to measure their skid resistance. A Findlay Irvine MK 2-D Grip Tester (GT) was used. High Friction Surfacing (HFS) and micro-surface materials showed the highest friction values. Although HFS showed approximately 15 percent more surface friction than the micro-surface section, the effectiveness of these two high surface friction paving materials requires longer term monitoring in the future as well as life-cycle cost analysis. SMA friction was approximately 10 percent lower than the City of Calgary Mix Type B-75. The difference in pavement friction of two SMA sections paved in 2010 and 2011 was not significant. Friction of the same generic paving material (Superpave or SMA) can be different. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".