Validation of Material-Level Performance Models
Bibliographic record
Abstract
A mechanistic pavement analysis with laboratory fatigue cracking and rutting models was validated with the response and performance measured from asphalt pavements. Asphalt pavements with different air void contents were tested using the third-scale Model Mobile Loading Simulator (MMLS3). The fatigue life prediction algorithm adopts a cumulative damage analysis; the permanent deformation prediction algorithm uses a sublayering method. These algorithms, which are similar to the ones adopted in the NCHRP 1–37A Mechanistic–Empirical Pavement Design Guide (MEPDG), account for the loading rate and temperature variation along the depth of the pavements. The major difference between the algorithms used in this study and the ones in the MEPDG is that the difference in loading frequencies between the laboratory test method and the MMLS3 test was accounted for in this study using the time–temperature superposition principle with growing damage. The predictions of fatigue life and permanent deformation growth in the MMLS3 tests revealed that the proposed algorithms do a reasonable job in predicting these parameters, although improved predictions may be achieved by adopting more fundamental models. It is expected that the resulting alliance between the accelerated pavement test, laboratory material level test, and performance models can serve as a cornerstone for the successful estimation of the service life of in situ pavements.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".