Different computer-operated temperature-controlled equipments are used to predict asphalt mixture performance in laboratory at approximately similar conditions to actual in-service pavements. Asphalt pavement analyzer (APA) is one such multi-functional sophisticated equipment. Wearing course layers are primarily attributable to be deteriorated by rutting distress in flexible asphalt pavements. Therefore, 30 representative wearing course mixtures commonly used in road pavements were tested for rutting distress using APA. Mixtures were evaluated based on their rutting performance and found that consistent ranking has been observed at temperature conditions of 40°C and 50°C. It was observed that APA rut depth could be effectively modelled using statistical techniques of multiple non-linear regression and artificial neural network. Novel aggregate material indices designated as ‘aggregate source index’ and ‘aggregate gradation index’ were developed in this study. Based on past literature; temperature, aggregate source index (ASI), aggregate gradation index, bitumen penetration values, and number of APA loading cycles/strokes were included as independent variables in the model developed for selected asphalt mixtures. It has been concluded from the values of non-linear regression parameters that temperature has the highest significance for rut depth prediction, followed by the bitumen penetration value, ASI, aggregate gradation index and number of loading cycles. Statistical parameters i.e. coefficient of determination, root mean squared error, values account for, and mean absolute percentage error showed that the artificial neural network technique better predicts the APA rut depth as compared to multiple non-linear regression techniques.

Rutting or plastic deformation is one of most frequently observed distress types in the wheel paths of flexible road pavements. This distress may cause the pavement structure to be attributable to early failure by expediting the deterioration due to load and environmental detrimental agents. Therefore, it is ideal to simulate the rutting performance of different asphalt mixtures in laboratory-controlled conditions before exposing them to aggressive field conditions. Different rutting simulation computer-controlled equipments are available to predict the mixture performance in laboratory at nearly similar conditions to actual in-service pavements. Cooper wheel tracking equipment is one such precise equipment. Mostly, the wearing course is likely to be damaged by this distress type in flexible asphalt pavements. Therefore, in this research study, thirty representative wearing course mixtures commonly used in road pavements were tested for rutting distress using Cooper wheel tracker. Three aggregate sources, three binders from two sources, five mixture gradations and two temperature conditions were included in the testing scheme. Mixtures were evaluated based on the terminal value of rut depth. Consistent ranking of mixtures has been observed for variable combinations of aggregates, binders, temperatures and gradations. Flakiness index (FI) of aggregates, binder penetration values, per cent coarse material retained on 4.75 mm sieve and temperature values are considered as independent variables based on past researches. The significance of these independent variables on the dependent variable of rut depth has been analysed by analytical tools of general full factorial design analysis. It has been concluded that temperature has highest significance for the rut depth prediction, followed by FI of aggregates, bitumen penetration value and percentage of coarse fraction.