The Colorado Department of Transportation (CDOT) recently concluded a study to implement
and adopt the American Association of State Highway and Transportation Officials (AASHTO)
Mechanistic-Empirical Pavement Design Guide (MEPDG) and its accompanying software into
its routine pavement design practice. Implementation of the MEPDG in Colorado required local
calibration of the MEPDG distress and smoothness prediction models. This paper discusses the
calibration of the “global” rutting model to account for Colorado’s unique climate, traffic, and
soils conditions as well as the various asphalt concrete (AC) mix types used in the State. A key
challenge during the local calibration effort was to produce different rutting coefficients for each
AC mix type, e.g., Marshall, Superpave, and polymer modified asphalt (PMA).
Local calibration of the MEPDG global models using CDOT input data was done using
nonlinear model optimization tool available in the SAS statistical software. AC rutting, unbound
aggregate base rutting, and subgrade rutting global model coefficients were adjusted through
local calibration. Four of the ten model coefficients were adjusted. The local calibration
coefficient βr1 turned out to be different for each of the three primary CDOT AC. As expected,
the PMA had the lowest value of the βr1 coefficient among three asphalt mixes, resulting in the
lowest AC rutting. The goodness of fit and bias test results indicate an adequate goodness of fit
with minimal bias. This local calibration effort improves the overall prediction accuracy of the
rutting model and its standard error.
