Superpave Design Aggregate Structure Considering Uncertainty: II. Evaluation of Trial Blends
Bibliographic record
Abstract
Abstract The current evaluation of Superpave design aggregate structure is deterministic. The design involves evaluation of selected trial blends based on volumetric, compaction, and dust proportion requirements. This article incorporates the uncertainties of all 17 variables involved in the process, measured by the coefficient of variation (CV), and develops a revised procedure for comparing mixture properties with the performance-based criteria. The uncertainties of eight measured properties are propagated through the calculation and make the uncertainty of some variables very large. Issues related to the reliability of some variables (CV > 25 %) are discussed and criteria to resolve them are established. The developed mathematical formulas of uncertainty were verified using Monte Carlo simulation. The results show that the potentially unreliable variables are as follows: volume of absorbed asphalt, percentage of absorbed asphalt, and percent voids in total mix at the design compaction level. A tool is provided to help the designer trace the uncertainty of the unreliable variables back to the measured properties so that their precisions may be revised. The current National Cooperative Highway Research Program–recommended precisions for specific gravities were found to be generally satisfactory. However, further research should continue to improve the precision of specific gravity measurements as some intermediate variables may still become unreliable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".