Local calibration of MEPDG for Superpave Pavements in Ontario : Enhance rutting calibration and preliminary study on IRI model
Bibliographic record
Abstract
The AASHTO Mechanistic-Empirical Pavement Design Guide requires local calibration to account for local conditions, materials, and engineering practices. Previous local calibration studies in Ontario focused mainly on permanent deformation models for pavement rutting. The objectives of this study are twofold. First, to provide an enhanced calibration for the rutting models by using more vigilantly cross-verified input data and updated observed rutting data. Second, to perform a trial calibration for the international roughness index (IRI) model by considering three different calibration methods. Cracking models calibration, being performed by another colleague, has not yet been finalized; therefore, the IRI model calibration cannot be finalized in this study. Based upon 63 Superpave sections, the local calibration coefficients were found to be βAC = 1.7692, βT = 1.0, βN = 0.6262, βGB = 0.0968 and βSG = 0.2787 , which reduced the standard deviation of residuals to a value of 1 mm. The IRI calibration study found that the initial IRI value plays an important role in the calibration. Keywords: International Roughness Index (IRI) model; local calibration; Mechanistic-Empirical Pavement Design Guide (MEPDG); rutting model; Superpave.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".