SENSITIVITY EVALUATION OF THE MECHANISTIC-EMPIRICAL PAVEMENT DESIGN GUIDE (MEPDG) FOR FLEXIBLE PAVEMENT PERFORMANCE PREDICTION
| dc.contributor.advisor | Schwartz, Charles W. | en_US |
| dc.contributor.author | Li, Rui | en_US |
| dc.contributor.department | Civil Engineering | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2013-07-04T05:34:58Z | |
| dc.date.available | 2013-07-04T05:34:58Z | |
| dc.date.issued | 2013 | en_US |
| dc.description.abstract | The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions under various feasible design scenarios. The MEPDG performance predictions for the anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize the pavement materials, layers, design features, and condition. This research focuses on comprehensive sensitivity analyses of flexible pavement performance predictions to MEPDG design inputs under five climatic conditions and three traffic levels. Design inputs evaluated in the analyses include traffic volume, layer thicknesses, material properties, groundwater depth, and others. Correlations among design inputs were considered where appropriate. Both local One-At-a-Time (OAT) sensitivity analyses and global sensitivity analyses (GSA) were performed. Two response surface modeling (RSM) approaches, multivariate linear regressions and artificial neural networks (ANN), were developed to model the GSA results for evaluation of MEPDG input sensitivities across the entire problem domain. The ANN-based RSMs were particularly effective in providing robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs. The design limit normalized sensitivity index (NSI) adopted in this study provides practical interpretation of sensitivity by relating a given percentage change in a MEPDG input to the corresponding percentage change in predicted distress/service life relative to its design limit value. The design inputs most consistently in the highest sensitivity categories across all distresses were the hot mix asphalt (HMA) dynamic modulus master curve, HMA thickness, surface shortwave absorptivity, and HMA Poisson's ratio. Longitudinal and alligator fatigue cracking were also very sensitive to granular base thickness and resilient modulus and subgrade resilient modulus. Additional findings are also provided for each specific pavement type. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/14326 | |
| dc.subject.pqcontrolled | Engineering | en_US |
| dc.subject.pqcontrolled | Civil engineering | en_US |
| dc.subject.pquncontrolled | Artificial Neural Network | en_US |
| dc.subject.pquncontrolled | Mechanistic-Empirical Pavement Design | en_US |
| dc.subject.pquncontrolled | MEPDG | en_US |
| dc.subject.pquncontrolled | Pavement Design Parameters | en_US |
| dc.subject.pquncontrolled | Sensitivity Analyses | en_US |
| dc.subject.pquncontrolled | Weather | en_US |
| dc.title | SENSITIVITY EVALUATION OF THE MECHANISTIC-EMPIRICAL PAVEMENT DESIGN GUIDE (MEPDG) FOR FLEXIBLE PAVEMENT PERFORMANCE PREDICTION | en_US |
| dc.type | Dissertation | en_US |