The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance models and the associated AASHTOWare® Pavement ME Design software are nationally calibrated using design inputs and distress data largely from the national Long-Term Pavement Performance (LTPP). Further calibration and validation studies are necessary for local highway agencies’ implementation by taking into acc...

creep compliance is one of the fundamental tests of mechanistic- empirical flexible pavement design procedure in the aashto 2002 design guide. in this research, a new artificial neural network model for estimating the hma creep compliance with the generalization ability of r=0.949 has been developed successfully using feed forward multi layer perceptron artificial neural networks (anns) with le...

The performance models used in the Mechanistic-Empirical Pavement Design Guide (MEPDG) are nationally calibrated with design inputs and performance data obtained primarily from the national Long-Term Pavement Performance database. It is necessary to verify and calibrate MEPDG performance models for local highway agencies' implementation by taking into account local materials, traffic informatio...