Calibration of MEPDG Performance Models for Flexible Pavement Distresses to Local Conditions of Ontario
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».