Superpave Design Aggregate Structure Considering Uncertainty: I. Selection of Trial Blends
Bibliographic record
Abstract
Abstract The existing method of selecting Superpave trial aggregate blends is deterministic and is based on trial-and-error. The primary purpose of this article is to develop a stochastic optimization model that includes the uncertainties of individual aggregate gradations, primary aggregate (PA) properties, and related specifications. The model can directly determine three different trial blends: (1) a blend close to the minimum specification limits, (2) a blend not close to the specification limits or to the restricted zone (RZ), and (3) a blend close to the maximum specification limits and to the RZ. The constraints of the model include gradation-control specifications, RZ limits, PA properties, and special and unity constraints. The PA properties include coarse aggregate fractured faces, fine aggregate angularity, sand equivalent, and flat and elongated particles. The uncertainty is formulated to ensure that the trial blends satisfy model constraints for a specified confidence level. A binary variable is used to allow the designer to produce a blend that passes below, above, or through the RZ. Application of the model is illustrated using a numerical example. The proposed model, which improves the reliability of trial blends and the efficiency of their selection, should be of interest to practitioners and researchers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".