Approaches for local calibration of mechanistic-empirical pavement design guide joint faulting model: a case study of Ontario
Why this work is in the frame
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Bibliographic record
Abstract
The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed by agencies as an innovative method for pavement design since the National Cooperative Highway Research Program (NCHRP) Project 1-37A was implemented in 2004. Over the years, the MEPDG has evolved into the AASHTOWare Pavement ME Design software (AASHTOWare®). Local calibration of the performance models in the AASHTOWare® is a crucial and challenging task to improve the effectiveness of its application. The accuracy of the calibration depends on efficient methods and validation processes. This paper aims at developing local calibration methods for joint faulting prediction model of Jointed Plain Concrete Pavement (JPCP). This study not only focuses on improving the prediction accuracy of the joint faulting model but also demonstrating the various optimisation procedures in detail. A total of 27 representative JPCP sections were used in the processes of calibration. Three optimisation approaches were used: (1) One-At-a-Time (OAT) through the trial-and-error procedure, (2) generalised reduced gradient (GRG) using MS Excel® Solver, and (3) Levenberg-Marquardt Algorithm (LMA) fitting the functions. The prediction accuracy of local models was improved as compared with the global ones. Average Bias (AB) reduced from 0.3083 to 0.0578, and Standard Error of the Estimate (SEE) reduced from 0.3345 to 0.1912. Among the three local calibration approaches, approach 2 and approach 3 had more significant improvement on results than approach 1. Finally, the integral procedures were provided for local calibration in Ontario.
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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 | 0.000 |
| Open science | 0.000 | 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 it