A probabilistic approach to identify the representative rut curve for a bituminous mixture specimen used in a dry wheel tracking test
Bibliographic record
Abstract
Rutting is considered a highly significant failure in bituminous pavements. Wheel tracking tests are widely used laboratory simulation tests to characterize the rutting resistance of bituminous mixtures. Considerable variation is observed in the accumulated rut depth at different locations along the wheel traverse and the representative rut curve obtained from different methodologies also shows significant variations. A probabilistic approach was adopted to analyze this scatter in the rut depth at a specific number of wheel passes and reliability-based rut curves were developed. Weibull and lognormal distributions are better at characterizing the scatter in the accumulated rut depths at various locations than the normal distribution. The results from the probabilistic rut data for two different binders and two different bituminous mixtures tested at six different temperatures at a specific number of wheel passes showed that different representative rut curves have different percentages of reliability. This work provides a rationale for choosing a representative rut curve from different methodologies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".