Investigation of the effect of nano-silica on thermal sensitivity of HMA using artificial neural network

Due to changes in weather conditions, engineers try to improve performance characteristics and properties of asphalt mixtures using additives. In this regard, modifiers are used because bitumen cannot have a good performance in all environmental and loading conditions. This paper examined the impact of one of the most efficient and widely used nanomaterials in various industries called nano-silica (Nano SiO2) on the thermal properties of bitumen and asphalt mixture. Using a solution that can reduce the cost and time of assessment is very important. Using artificial neural networks in many cases facilitates operations on data engineering sciences. It is necessary to ensure that a comprehensive study is performed through considering all or most of the parameters affecting the behavior. The aim of this study was to evaluate temperature sensitivity of asphalt mixture modified with nano-silica. To achieve this goal, 5 types of mixtures with different percentage of nano-silica (0, 0.2, 0.4, 0.7 & 0.9%) were prepared and the temperature sensitivity of modified mixtures was evaluated. Results showed that nano-silica can improve the temperature sensitivity of asphalt mixtures. Moreover, the thermal sensitivity of asphalt mixtures modified with nano-silica using artificial neural network and methods of multilayer perceptron (MLP) and radial basis function (RBF) was simulated and analyzed.