Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures

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@Article{Gandomi:2010:JMCE,

• author = "Amir Hossein Gandomi and Amir Hossein Alavi and Mohammad Reza Mirzahosseini and Fereidoon Moghadas Nejad",
• title = "Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures",
• journal = "ASCE Journal of Materials in Civil Engineering",
• year = "2011",
• volume = "23",
• number = "3",
• pages = "248--263",
• month = mar,
• email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
• keywords = "genetic algorithms, genetic programming, gene expression programming, Marshall mix design, Formulation",
• ISSN = "0899-1561",
• URL = "https://ascelibrary.org/toc/jmcee7/23/3",
• DOI = "doi:10.1061/(ASCE)MT.1943-5533.0000154",
• size = "16 pages",
• abstract = "Rutting has been considered as the most serious distresses in flexible pavements for many years. Flow number is an explanatory index for the evaluation of rutting potential of asphalt mixtures. In this study, a promising variant of genetic programming, namely gene expression programming (GEP) is used to predict the flow number of dense asphalt-aggregate mixtures. The proposed constitutive models relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air voids, percentage of voids in mineral aggregate, Marshall stability and flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uniaxial dynamic creep tests conducted in this study. Relative importance values of various predictor variables were calculated to determine their contributions to the flow number prediction. A multiple least squares regression (MLSR) analysis was performed using the same variables and data sets to benchmark the GEP models. For more verification, a subsequent parametric study was carried out and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed correlations are effectively capable of evaluating the flow number of asphalt mixtures. The GEP-based formulae are simple, straightforward and particularly valuable for providing an analysis tool accessible to practising engineers.",
• notes = "1Research Assistant, National Elites Foundation, Tehran, Iran & College of Civil Engineering, Tafresh University, Tafresh, Iran. 2PhD Student, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland. 3Assistant Professor, College of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. 4Assistant Professor, College of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.",

}

Genetic Programming entries for A H Gandomi A H Alavi Mohammad Reza Mirzahosseini Fereidoon Moghaddas Nejad

Citations