Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming

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@Article{azarhoosh:2020:AJSE,

• author = "Alireza Azarhoosh and Salman Pouresmaeil",
• title = "Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming",
• journal = "Arabian Journal for Science and Engineering",
• year = "2020",
• volume = "45",
• number = "10",
• pages = "8427--8441",
• keywords = "genetic algorithms, genetic programming, Hot-mix asphalt, Marshall mix design, Index of aggregate particle shape and texture (particle index), Viscosity of bitumen, Genetic programming method",
• URL = "http://link.springer.com/article/10.1007/s13369-020-04776-0",
• DOI = "doi:10.1007/s13369-020-04776-0",
• size = "15 pages",
• abstract = "The mix design of asphalt concrete is usually accomplished in the Iranian ministry of road and transportation according to the Marshall method. Marshall mix design parameters are a function of grading and properties of aggregates, amount and type of bitumen in asphalt mixtures. Therefore, in order to determine these parameters and the optimum bitumen content, many samples with different compounds and conditions must be manufactured and tested in the laboratory, a process that requires considerable time and cost. Accordingly, the necessity of using new and advanced methods for the design and quality control of asphalt mixtures is becoming more and more evident. Therefore, in this study, a genetic programming simulation method was employed to predict the Marshall mix design parameters of asphalt mixtures. Also, multiple linear regression models were adopted as the base model to evaluate the models presented by the genetic programming method. The models proposed here predict the Marshall mix design parameters based on parameters such as the index of aggregate particle shape and texture, the amount and viscosity of the bitumen. The results demonstrated that the proposed methods are more efficient than the costly laboratory method, and genetic programming models with minimal error (identified in this study with RMSE and MAE parameters) and correlation coefficients > 0.9 can predict relatively accurate Marshall mix design parameters.",

}

Genetic Programming entries for Alireza R Azarhoosh Salman Pouresmaeil

Citations