Stiffness performance of polyethylene terephthalate modified asphalt mixtures estimation using support vector machine-firefly algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Soltani:2015:Measurement,
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author = "Mehrtash Soltani and Taher Baghaee Moghaddam and
Mohamed Rehan Karim and Shahaboddin Shamshirband and
Ch Sudheer",
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title = "Stiffness performance of polyethylene terephthalate
modified asphalt mixtures estimation using support
vector machine-firefly algorithm",
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journal = "Measurement",
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volume = "63",
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pages = "232--239",
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year = "2015",
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ISSN = "0263-2241",
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DOI = "doi:10.1016/j.measurement.2014.11.022",
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URL = "http://www.sciencedirect.com/science/article/pii/S0263224114005831",
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abstract = "Predicting asphalt pavement performance is an
important matter which can save cost and energy. To
ensure an accurate estimation of performance of the
mixtures, new soft computing techniques can be used. In
this study, in order to estimate the stiffness property
of Polyethylene Terephthalate (PET) modified asphalt
mixture, different soft computing methods were
developed, namely: support vector machine-firefly
algorithm (SVM-FFA), genetic programming (GP),
artificial neural network (ANN) and support vector
machine. The support vector machine-firefly algorithm
(SVM-FFA) is a metaheuristic search algorithm developed
according to the socially dashing manners of fireflies
in nature. To develop the models, experiments were
performed. The process, which simulates the mixtures'
stiffness, was created with a soft computing method,
the inputs being PET percentages, stress levels and
environmental temperatures. The performance of the
proposed system was confirmed by the simulation
results. Soft computing methodologies show very good
learning and prediction capabilities and the results
obtained in this study indicate that the SVM-FFA
contributed the most significant effect on stiffness
performance estimation since the SVM-FFA model had a
better correlation coefficient than the SVM, ANN and GP
approaches. R2 and RMSE were used for making
comparisons between the expected and actual values of
SVM-FFA, GP, ANN and SVM. The proposed SVM-FFA
methodology predicted the output values with 254.4743
(mm/day) and 0.9957 RMSE and R2 respectively.",
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keywords = "genetic algorithms, genetic programming, Firefly
algorithm, Support vector machine, Pavement
performance, PET modified asphalt mixtures,
Environmental conditions, Estimation",
- }
Genetic Programming entries for
Mehrtash Soltani
Taher Baghaee Moghaddam
Mohamed Rehan Karim
Shahaboddin Shamshirband
Sudheer Ch
Citations