The decreasing supply of soils with geotechnical parameters suitable for pavement designs is a visible problem in our environment. In order to establish more efficient designs and adequate construction criteria, it is essential to understand the performance of materials. This is a study of the permanent deformation (PD) of soil used in pavement layers, obtaining prediction models through the technique of artificial neural networks, in addition to the design of pavement structures using mechanistic-empirical and empirical methods. The multistage repeated load triaxial (RLT) test, as well as numerical analyses of stresses and displacements using the CAP3D program, was used. The results showed that both the test procedure and the prediction models performed satisfactorily in obtaining PD behavior. Moreover, designs using the methods adopted resulted in distinct structures, that is, thickness different from the granular pavement layers. It was concluded that the model and test procedure exhibit significant potential for characterizing and modeling the PD of granular materials.