Predicting fatigue service life reductions of asphalt pavements due to deficiency in design-level values of effective binder content
Bibliographic record
Abstract
The focus of this study is the evaluation of the effects of deficiencies in design-level values of effective binder content (Δ Vbe) on the top-down (TD) and bottom-up (BU) fatigue cracking performance of hot-mix asphalt (HMA) mixtures using AASHTOWare Pavement ME Design structural simulation program. Using an analytical–mathematical-based methodology, this study also aims to predict the reduction in fatigue service life (i.e., TD, and BU fatigue cracking) of asphalt pavements that are most sensitive to the variation (Δ Vbe). Two types of mixes with varying levels of Vbe are used to simulate the fatigue cracking performance of a full-depth (FD) pavement structure. For each of the three studied cases, one asphalt course has a variable value of Vbe, while the two other courses have a fixed value of Vbe. Results indicate that the Δ Vbe of 3% reduced 50%–70% of fatigue cracking service life. Additionally, Pavement ME shows a limitation to account for the effect of Vbe in the intermediate course on the BU fatigue cracking.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".