Calibration of MEPDG Performance Models for Flexible Pavement Distresses to Local Conditions of Ontario
Bibliographic record
Abstract
The implementation of the American Association of State Highway and Transportation Officials (AASHTO) Mechanistical-Empirical Pavement Design requires the development of a design procedure that can be used by the agencies and engineering consultants to design new and reconstructed rigid and flexible pavements. To calibrate the design procedure for a region, a large dataset representing the particular local conditions is needed. It includes traffic, climate, site material characteristics, performance requirements and historical data. The performance models were calibrated in North America using the Long Term Pavement Database Program (LTPP), therefore, the models must be calibrated to local conditions in order to obtain more suitable parameters, formulas and predictions. It is expected that calibrated performance models using site-specific data will predict pavement performance approximated to the performance measured in the field. Gathering data related with observed distresses is essential for subsequent comparison with predicted distresses. \nThe primary objective of this project is to calibrate the performance models of flexible pavement distresses, including total rutting (permanent deformation) and asphalt concrete (AC) bottom-up fatigue cracking, to the local conditions of new flexible pavement in Ontario, Canada. \nSixteen (16) representative pavement sections from widening and reconfiguration highway projects were selected. Performance data, traffic data, structure information, materials properties and performance data were obtained from site-specific investigation and pavement design reports provided by the Ministry of Transportation Ontario (MTO). \nThe AASHTOWare Pavement ME DesignTM was used to run the initial predictions using the global calibration coefficients. Then, the obtained predicted distresses were compared with the measured distresses to assess for local bias and goodness of fit. The analysis showed that, using the global calibration coefficients, the AASHTOWare model under predicted alligator cracking and over predicted total rutting. Statistical analysis, such as, Regression Analysis and the Microsoft Solver numerical optimization routine were used to find the regression coefficients, using the approach of minimizing the sum of squared error (SSE). \nConcerning alligator cracking, the local calibration factors have improved the bias and standard error of the estimate (SEE). Plots also showed that points are randomly scattered along equality line and predicted values closer to the measured values. \nRegarding permanent deformation (rutting), the local calibration factors have improved the bias and standard error of the estimate. The accuracy of the transfer function has increased in comparison to the use of the global calibration values, suggesting that the local calibration procedure has improved the rutting model. Analyzing the plots measured versus predicted, points are better scattered and a shift is clearly noted in the chart from global to local calibration, indicating that local calibration coefficients improved distress estimations.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".