Effectiveness of static and dynamic backcalculation approaches for asphalt pavement
Bibliographic record
Abstract
In this study, the field falling weight deflectometer (FWD) data for asphalt pavement with various base types were backcalculated through dynamic and static backcalculation approaches, and the effectiveness of backcalculation approaches was studied. Asphalt concrete (AC) was treated as a viscoelastic material and the complex modulus was obtained using the dynamic approach. The dynamic modulus at a fixed frequency was computed for comparison purposes. The coefficient of variance and the compensating layer effect were assumed as two characteristics for the effectiveness of backcalculation approaches. The results show that the layer property from the dynamic backcalculation approach for different stations were more consistent and showed smaller coefficient of variance, which were more appropriate for the characterization pavement behavior. The elastic moduli from the static approach were more variable and exhibited a compensating layer effect in which a portion of the modulus of one layer was backcalculated into other layers. The dynamic approach is more effective than static approaches in backcalculation of layer properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".