Marshall stability and flow analysis of asphalt concrete under progressive temperature conditions: An application of advance decision-making approach

Abstract

The behaviour of asphalt concrete mixtures is difficult to understand due to its complex nature under different loading conditions and environmental factors. For prediction, there is a need to find mathematical relations between multiple inputs and outputs using a simple and precise way. Recently, artificial neural networks (ANNs) has been widely used to study the mechanical parameters of asphalt concrete materials and its applications in civil engineering fields. This study presents the application of ANNs method for prediction of Marshall stability of asphalt concrete developed with two different types of aggregates based on mineralogy under four different testing temperatures ranging between 25 °C and 60 °C. The ANNs model established with six input variables including temperature, aggregate type, ultrasonic pulse velocity–time and space volume, unit volume of dry air, and saturated surface dry weight. The proposed model developed using six neurons in hidden layer for the prediction of experimental data. The feasibility of the proposed model checked in terms of root mean square error (RMSE) and coefficient of determination (R2). The R2 values found within range during both training (0.909–0.999) and validation phase (0.886–0.997) depending on estimated parameters. Moreover, the influence of different aggregate type has been investigated under varying temperatures conditions using the proposed ANNs method. The proposed model has shown the potential to understand the mechanical behaviour of sustainable asphalt concretes accurately under various temperature conditions.