Modelling asphalt pavement analyzer rut depth using different statistical techniques

Abstract

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.