Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates

Due to the complex behavior of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the permanent deformation of asphalt pavement is difficult. To predict, it is required to find the mathematical relation between the input and output data by an accurate and simple method. In recent years, artificial neural networks (ANNs) have been used to model the properties and behavior of materials, and to find complex relations between different properties in many fields of civil engineering applications, because of their ability to learn and to adapt. This study discusses the application of ANN in predicting permanent deformation of asphalt concrete mixtures modified by Nano-additives. A total number of 270 asphalt mixtures were constructed from two different aggregate sources (natural and steel slag) and were modified by micro silica and Nano TiO2/SiO2. All samples were tested at three different testing temperatures of 40, 50, and 60 degrees C and five stresses of 100-500 kPa. An ANN model developed using five input parameters including: aggregate source, additive type, additive content, temperature, and stress. An ANN with 10 neurons in hidden layer was considered as the appropriate architecture for predicting final strain of asphalt mixtures, and an excellent conformity was observed between the predicted and the test data. The result indicates that the proposed model can be applied in predicting final strain of asphalt mixtures. The model is further applied to evaluate the effect of different percentages of Nano-additive on permanent asphalt deformation. Results show that an increase in percentage of Nano-additives is very effective in reducing the final strain of asphalt mixtures. However, an increase in percentage of additives over 8.5% does not help to reduce permanent deformation under dynamic loading in the asphalt mixtures. (C) 2015 Elsevier Ltd. All rights reserved.