The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Moghaddam:2016:Measurement,
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author = "Taher Baghaee Moghaddam and Mehrtash Soltani and
Hamed Shahrokhi Shahraki and Shahaboddin Shamshirband and
Noorzaily Bin Mohamed Noor and Mohamed Rehan Karim",
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title = "The use of SVM-FFA in estimating fatigue life of
polyethylene terephthalate modified asphalt mixtures",
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journal = "Measurement",
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volume = "90",
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pages = "526--533",
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year = "2016",
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ISSN = "0263-2241",
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DOI = "doi:10.1016/j.measurement.2016.05.004",
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URL = "http://www.sciencedirect.com/science/article/pii/S0263224116301440",
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abstract = "To predict fatigue life of Polyethylene Terephthalate
(PET) modified asphalt mixture, various soft computing
methods such as Genetic Programming (GP), Artificial
Neural Network (ANN), and Fuzzy Logic-based methods
have been employed. In this study, an application of
Support Vector Machine Firefly Algorithm (SVM-FFA) is
implemented to predict fatigue life of PET modified
asphalt mixture. The inputs are PET percentages, stress
levels and environmental temperatures. The performance
of proposed method is validated against observed
experiment data. The results of the prediction using
SVM-FFA are then compared to those of applying ANN and
GP approach and it is concluded that SVM-FFA leads to
more accurate results when compared to observed
experiment data.",
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keywords = "genetic algorithms, genetic programming, Firefly
algorithm, Support vector machine, PET modified asphalt
mixtures, Environmental conditions, Fatigue life",
- }
Genetic Programming entries for
Taher Baghaee Moghaddam
Mehrtash Soltani
Hamed Shahrokhi Shahraki
Shahaboddin Shamshirband
Noorzaily Bin Mohamed Noor
Mohamed Rehan Karim
Citations