Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique

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@Article{Mirabdolazimi:2017:CBM,

• author = "S. M. Mirabdolazimi and Gh. Shafabakhsh",
• title = "Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique",
• journal = "Construction and Building Materials",
• volume = "148",
• pages = "666--674",
• year = "2017",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2017.05.088",
• URL = "http://www.sciencedirect.com/science/article/pii/S0950061817309753",
• abstract = "The most significant problems in the maintenance of highway networks are low strength against dynamic loads and short service life of pavements. In recent years using additive materials to improve the performance of asphalt mix under dynamic loading has been remarkably developed. Previous research show that adding appropriate polymer materials to hot mix asphalt improves the dynamic properties of these mixtures. A series of dynamic creep test were conducted under different temperatures and stress levels to evaluate rutting performance of asphalt samples. The proposed artificial neural networks (ANN) model for rutting depth has shown good agreement with experimental results. Beside, in this study a comparison is made between the Burgers model and genetic programming (GP) model in estimating the rutting depth of asphalt mix. Performance of the genetic programming model is quite satisfactory. The obtained results can be used to provide an appropriate approach to enhance the performance of asphalt pavements under dynamic loads.",
• keywords = "genetic algorithms, genetic programming, HMA, Rutting depth, Forta fiber, Artificial neural networks",

}

Genetic Programming entries for S M Mirabdolazimi Gholamali Shafabakhsh

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