A non-destructive approach for the predictive master curve of ASPHALT pavements using ultrasonic and deflection methods
Bibliographic record
Abstract
The elastic modulus of an asphalt mixture varies with temperature and frequency of load due to its visco-elastic nature, and this behaviour is represented by a master curve constructed by either laboratory tests or predictive models based on known material properties. The predictive approach is based on the material specifications and enables to estimate the modulus over a range of frequency without any laboratory tests. However, this estimated curve should be corrected with respect to a measured reference value, which can be obtained from non-destructive methods. In this study, ultrasonic surface waves (USW) and light weight deflectometer (LWD) tests are conducted on two laboratory slab specimens and an as built pavement, and the experimental results are compared with the predictive master curves. The measured dynamic moduli are found consistently higher than the predictive master curves indicating that the model underestimates the modulus of asphalt mix. Finally, the method is verified by shifting the moduli measured at different frequencies to the 25-Hz-design modulus, of which those obtained by the USW tests from the laboratory and field match very well, whereas those by the USW and the LWD tests from the as built pavement are also found highly consistent with each other.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".