Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm
来源期刊：中南大学学报(英文版)2015年第5期
论文作者：Morteza Vadood Majid Safar Johari Ali Reza Rahai
文章页码：1937 - 1946
Key words：hot mix asphalt; fatigue property; reinforced fiber; artificial neural network; genetic algorithm
Abstract: While various kinds of fibers are used to improve the hot mix asphalt (HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network (ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm (GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy (correlation coefficient of 0.96).
Morteza Vadood1, Majid Safar Johari1, Ali Reza Rahai2
(1. Textile Engineering Department, Amirkabir University of Technology, Tehran, Iran;
2. Civil and Environmental Engineering Department, Amirkabir University of Technology, Tehran, Iran)
Abstract:While various kinds of fibers are used to improve the hot mix asphalt (HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network (ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm (GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy (correlation coefficient of 0.96).
Key words:hot mix asphalt; fatigue property; reinforced fiber; artificial neural network; genetic algorithm
