Development of regression equations for local calibration of rutting and IRI as predicted by the MEPDG models for flexible pavements using Ontario's long-term PMS data
Bibliographic record
Abstract
Local calibration is an important step before a transportation agency adopts the American Association of State Highway and Transportation Officials' (AASHTO) mechanistic-empirical pavement design guide (MEPDG). This paper presents the challenges of and findings from the local calibration of flexible pavements in provincial highways under the jurisdiction of the Ministry of Transportation of Ontario (MTO). A calibration database was developed that involved a hierarchical framework of the input parameters required for AASHTOWare Pavement ME (the MEPDG software) and the historical field performance data based on the MTO's second-generation pavement management system. A regression analysis is carried out for preliminary calibration of rutting and international roughness index (IRI) models by comparing the predicted distress to observed distress. The analysis suggested that whereas the MEPDG provided fairly unbiased prediction of the IRI value, it often over-predicted the total rutting. Calibrated predicted IRI and rut depth are found for Ontario's local conditions from MEPDG distress prediction models. A further clustering analysis based on Functional Class and geographical zone for the rutting and IRI, respectively, improved the precision of the locally calibrated models.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".