A new approach for modeling of flow number of asphalt mixtures

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@Article{Alavi:2017:ACME,

• author = "Amir H. Alavi and Hassene Hasni and Imen Zaabar and Nizar Lajnef",
• title = "A new approach for modeling of flow number of asphalt mixtures",
• journal = "Archives of Civil and Mechanical Engineering",
• volume = "17",
• number = "2",
• pages = "326--335",
• year = "2017",
• ISSN = "1644-9665",
• DOI = "10.1016/j.acme.2016.06.004",
• URL = "http://www.sciencedirect.com/science/article/pii/S1644966516300814",
• abstract = "Flow number of asphalt-aggregate mixtures is an explanatory parameter for the analysis of rutting potential of asphalt mixtures. In this study, a new model is proposed for the determination of flow number using a robust computational intelligence technique, called multi-gene genetic programming (MGGP). MGGP integrates genetic programming and classical regression to formulate the flow number of Marshall Specimens. A reliable experimental database is used to develop the proposed model. Different analyses are performed for the performance evaluation of the model. On the basis of a comparison study, the MGGP model performs superior to the models found in the literature.",
• keywords = "genetic algorithms, genetic programming, Asphalt mixture, Flow number, Marshall mix design",

}

Genetic Programming entries for A H Alavi Hassene Hasni Imen Zaabar Nizar Lajnef

Citations