Rigid Pavement: Ontario Calibration of the Mechanistic-Empirical Pavement Design Guide Prediction models
Bibliographic record
Abstract
The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been introduced to transportation agencies as an innovative method for analysis and design of pavements. However, the MEPDG cannot be used by highway agencies without calibration due to the different situations in Canada. Local calibration the MEPDG, which means adjusting the coefficients of performance prediction models to meet the local conditions, should be an essential step for any agencies before the official acceptation of the MEPDG. \n \nAs the part of the project, Local Calibration of the MEPDG Prediction Models Using More Accurate Field Measurements funded by Highway Infrastructure Innovation Funding Program (HIIFP), this research involved the local calibration of the models for Jointed Plain Concrete Pavement (JPCP) in the Province of Ontario. Using the field measurements collected by Ministry of Transportation in Ontario (MTO), the study performed local calibration for 32 rigid (JPCP) pavement sections. The primary objective of this study was to examine prediction results using global models; then if not agree with the measurements, refine the coefficients of the MEPDG performance models using the nonlinear optimization methods. The proposed calibration was applied by using the sections located in different zones throughout Ontario to represent the local features, including climate and traffic conditions. Finally, the local calibration results are presented and compared with previous results of global models to assess the robustness of local calibration. The research shows the feasibility of the mathematical optimization method for in local calibration in Ontario, and it also provides some useful findings for future uses of the MEPDG.
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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 | 0.001 |
| Open science | 0.001 | 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".