Experimental validation of laboratory performance models using the third scale accelerated pavement testing
Bibliographic record
Abstract
Laboratory models for fatigue cracking and permanent deformation growth are validated using the response and performance measured from asphalt pavements with different air void contents under the third scale Model Mobile Loading Simulator (MMLS3). The fatigue life prediction algorithm is developed based on a cumulative damage concept and the algorithm for permanent deformation prediction involves a sub-layering method, dividing a pavement layer into several artificial layers for analysis. These algorithms account for the effects of applied loading rate and temperature variation along the pavement depth. The difference in loading frequencies between the laboratory experiments and the MMLS3 test was taken care of using the time-temperature superposition principle with growing damage. The proposed methodology is found to be reasonable in predicting fatigue life and permanent deformation growth in the MMLS3 tests. It is found that the resulted alliance among the accelerated pavement test, laboratory test, and performance models could serve as a foundation for the successful estimation of pavements’ service life in the future.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".