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Abstract The major goal of this work was to forecast the rut depth on road pavements under various loading, material, and temperature conditions. It is believed that the pavement system consists of multiple elastic layers, each of which has a specific Resilient Modulus (MR) and Poisson ratio. The commonly used CBR and ERA/AASHTO design guide charts for evaluating the total required trial depth, sub-grade modulus, and other bound and unbound layers’ resilient modulus control the empirical data used for analysis in the software. Trial depths used in all models for analysis using finite elements were in line with the standardized minimum thickness for the pavement’s individual layers in practice. At the later stage of the research, predictive rutting distress for all models derived from finite element analysis were plotted to see how they react to the series of required data when varied (CBR of: sub grade (5–7%), sub base (20–45%), base course (50–80%) and asphaltic concrete layer (3100MPa to 3500MPa), axle load: single axle to tandem, depth of sub-base and base course were varied from 150 mm to 180 mm) and while some were kept constant (temperature 30oC). Predictive rutting calculated ranged from (1. 989 to 4. 51 x 10^-6) mm. To improve pavement resistance to rutting and other distresses, there must be a considerable increase in the resilient modulus of individual layers. From this research, an increase in pavement subgrade and subbase CBR gave a more positive result. High traffic loads could cause the road to rut faster. This was detected when the axle arrangement was changed from single to tandem axles. The results given were still valid, and they could be used to project the rutting depth for any number of loading repetitions using linear progression. Rutting depths derived were quite small due to the low load repetition modeled during simulation.
Garoma et al. (Wed,) studied this question.