Bibliographic record
Abstract
Abstract The existing methods of asphalt mixture design are deterministic and rely only on the means of design parameters, such as unit weight and volumetric properties. This article presents a new design method of asphalt mixtures that considers the uncertainties of the measured properties and the calculated design parameters, represented by the coefficient of variation (CV). The uncertainties of the measured properties (typically CV < 1 %) propagate through the calculations and could result in high uncertainties in the calculated design parameters (e.g., CV > 40 %) that make them unreliable. The proposed method is developed in the context of the Marshall mix-design method and is applicable to the Superior Performing Asphalt Pavements method with minor modifications. The Taylor series expansion was used to develop the moments (mean and standard deviation) and CV of the calculated design parameters. The developed formulas were verified using Monte Carlo simulation. Criteria for sample acceptance are then presented based on the uncertainties of the design parameters. The uncertainty information is then used to establish confidence intervals for the design parameters, determine the optimum asphalt content, and compare the results with project specifications. Sensitivity analysis of the mix-design parameters is conducted, and practical implications are discussed. Numerical examples are presented to demonstrate the application of the proposed method. The proposed method takes the design of asphalt mixtures one step further toward a reliable performance-based design. As such, the method should be of interest to pavement engineers and practitioners.
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".