Methodology development and local calibration of MEPDG permanent deformation models for Ontario's flexible pavements
Bibliographic record
Abstract
The AASHTO-supported interim Mechanistic-Empirical Pavement Design Guide (MEPDG) was published in 2008 for trial use. However, local calibration of the empirical distress models in the design guide has been an essential exercise for any transportation agency before it formally adopts the MEPDG for practical design use. Nevertheless, a universally agreed upon local calibration methodology using historical pavement performance data from field evaluation has not been available. This research focuses on permanent deformation or rutting models in MEPDG and presents a layer-by-layer longitudinal local calibration process at the DARWin-METM platform coupled with Excel Macros. Using the best input data available, many of Level 3 accuracy, the study performs local calibration for 10 reconstructed and 19 rehabilitated flexible pavement sections. Further, this thesis statistically evaluated the level-3 pre-overlay rut value for rehabilitated flexible pavements in Ontario. A comparison of the longitudinal calibration and pooled local calibration demonstrated the importance of the longitudinal calibration in the quantification of uncertainties involved in local calibration.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".