Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

Abstract

Up to now various kinds of fibers are used to improve the hot mix asphalt (HMA) performance, but a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, in this paper, the resilient modulus of the modified HMA samples using polypropylene and polyester fibers (hybrid and single modes) was evaluated and modeled by regression method and artificial neural network (ANN). As ANN includes different parameters such as the number of neurons in hidden layer influenced on the prediction accuracy, genetic algorithm (GA) was used to optimize the ANN parameters. Also, GA parameters were optimized using the trial and error method such as the population size. The obtained results indicated that the optimized ANN with two hidden layers and two neurons in each hidden layer can predict the resilient modulus of fiber-reinforced HMA with high accuracy (correlation coefficient: .96).

Keywords:

• hot mix asphalt
• resilient modulus
• fiber reinforced
• artificial neural network
• genetic algorithm