Abstract
The development of an Artificial Neural Network (ANN) model to estimate subgrade resilient modulus is described in this paper. Nine (9) different sources of subgrade materials locally available in Georgia were subjected to the resilient modulus test with two replicates. The stress state and physical properties on resilient behavior of subgrade soils were successfully correlated with an ANN model developed in this paper. The results demonstrated that the stress state and physical properties of subgrade soil significantly influenced the subgrade resilient modulus, which in turn has a substantial effect on the pavement response predictions that impact pavement design.
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