Asphalt Mix Design Optimization for Efficient Plant Management
Bibliographic record
Abstract
The role of aggregate gradation in hot-mix asphalt performance is well documented in the literature. Yet the Bailey method is the only tool available for guidance on aggregate gradation selection for optimal performance. Also, there is a lack of tools for design engineers and plant managers of quarry sites to manage stockpile inventory levels and control cost of aggregate used in asphalt mixes. This work presents a linear programming model of the asphalt mix design problem and a numerical algorithm to solve the model. The algorithm is implemented in MATLAB as an asphalt mix design optimization (AMIDO) program. The program is successfully verified with an example. The results show that using the Bailey method alone results in suboptimal results and that cheaper mixes with similar aggregate ratios can be designed with the same aggregate stockpiles. For the specific stockpiles used in the verification, the AMIDO mix design resulted in a 53-cent/ton reduction in aggregate cost. This work improves the state of the art in asphalt mix design for dense-graded mixes and could be modified for other mixes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".