Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming
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- @Article{azarhoosh:2020:AJSE,
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author = "Alireza Azarhoosh and Salman Pouresmaeil",
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title = "Prediction of Marshall Mix Design Parameters in
Flexible Pavements Using Genetic Programming",
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journal = "Arabian Journal for Science and Engineering",
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year = "2020",
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volume = "45",
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number = "10",
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pages = "8427--8441",
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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",
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URL = "http://link.springer.com/article/10.1007/s13369-020-04776-0",
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DOI = "doi:10.1007/s13369-020-04776-0",
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size = "15 pages",
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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