Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm

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• 作者：Morteza Vadood ; Majid Safar Johari ; Ali Reza Rahai
• 关键词：hot mix asphalt ; fatigue property ; reinforced fiber ; artificial neural network ; genetic algorithm
• 刊名：Journal of Central South University of Technology
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：22
• 期：5
• 页码：1937-1946
• 全文大小：1,262 KB
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• 作者单位：Morteza Vadood (1)
  Majid Safar Johari (1)
  Ali Reza Rahai (2)
  
  1. Textile Engineering Department, Amirkabir University of Technology, Tehran, Iran
  2. Civil and Environmental Engineering Department, Amirkabir University of Technology, Tehran, Iran
  
• 刊物类别：Engineering
• 刊物主题：Engineering, general
  Metallic Materials
  Chinese Library of Science
  
• 出版者：Central South University, co-published with Springer
• ISSN：2227-5223

文摘

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).