  The performance of rigid pavement is greatly affected by the properties of
base/subbase as well as subgrade layer. However, the performance predicted by
the AASHTOWare Pavement ME design shows low sensitivity to the properties of
base and subgrade layers. To improve the sensitivity and better reflect the
influence of unbound layers a new set of improved models i.e., resilient
modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this
study. An Artificial Neural Network (ANN) model is developed to predict the
modified k-value based on finite element (FE) analysis. The training and
validation datasets in the ANN model consist of 27000 simulation cases with
different combinations of pavement layer thickness, layer modulus and slab-base
interface bond ratio. To examine the sensitivity of modified MR and k-values on
pavement response, eight pavement sections data are collected from the
Long-Term Pavement performance (LTPP) database and modeled by using the FE
software ISLAB2000. The computational results indicate that the modified MR
values have higher sensitivity to water content in base layer on critical
stress and deflection response of rigid pavements compared to the results using
the Pavement ME design model. It is also observed that the k-values using ANN
model has the capability of predicting critical pavement response at any
partially bonded conditions whereas the Pavement ME design model can only
calculate at two extreme bonding conditions (i.e., fully bonding and no
bonding).
