Local calibration of cracking models of MEPDG for Ontario's flexible pavement
Bibliographic record
Abstract
The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) introduces a pavement design method which uses both the mechanistic analyses and empirical models to predict pavement distresses and performance, which needs to be calibrated to local conditions and engineering practices based on local pavement performance data. This thesis focuses on the local calibration of fatigue (both bottom-up and top-down) and thermal cracking models in MEPDG for superpave flexible pavements on Ontario’s highways. Simulations were run in the software, after developing a calibration database of Ontario’s provincial highway and the predicted data is compared to the observed data. Significant difference is found in the comparisons which need to be minimized by calibrating the distress models. A new regression model is used to optimize the calibration parameters by minimizing the standard deviations of the residuals between the predicted and observed distresses. The challenges encountered and concluding remarks developed during the local calibration process are discussed. Keywords: Local Calibration, Mechanistic Empirical Pavement Design Guide (MEPDG), Cracking Models, Fatigue Cracking, Thermal Cracking, 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".