Creating a Performance-Based Asphalt Mix Design to Incorporate Oil Sand
Bibliographic record
Abstract
The sustainability of pavement projects can be improved by incorporating locally available materials. It has been shown that successful utilization of local materials is beneficial for the local economy, the environment, the contractor, and the owner. However, the use of innovative materials is often inhibited by current method-specifications. The objective of this research was to use performance-based testing and modern mix design tools to develop a special provision allowing for the use of oil sand in a bituminous pavement. Oil sand is a naturally occurring petroleum deposit comprised of heavy bitumen, sand, clay and water. The largest oil sand deposits, located in western Canada, are well known for crude oil production from oil sand. Oil sand is also available throughout the United States in smaller deposits where converting oil sand to crude oil is not feasible. In certain areas immediately surrounding oil sand deposits, contractors have been paving with oil sand asphalt mixtures for over a century; even though, modern mix-design methods have not been used to characterize these mixtures. To address this need and allow for contractors to use oil sand pavements on state funded jobs, a special provision was created for the Utah Department of Transportation utilizing oil sand from eastern Utah. The steps followed to create this provision were: oil sand characterization, volumetric-mix design, and laboratory testing. The oil sand was characterized by determining the bitumen content, the gradation, and temperature susceptibility. The mix design was created following a procedure similar to the method used to gap-graded hot mix asphalt. The special provision created from this research outlines the required oil sand characterization and laboratory testing that must be completed to design an oil sand asphalt pavement. Although this research concentrated on incorporating oil sand into bituminous mixtures, the methodology developed could be extended to provide a performance based testing framework to any innovative material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".