The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures

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@Article{Moghaddam:2016:Measurement,

• author = "Taher Baghaee Moghaddam and Mehrtash Soltani and Hamed Shahrokhi Shahraki and Shahaboddin Shamshirband and Noorzaily Bin Mohamed Noor and Mohamed Rehan Karim",
• title = "The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures",
• journal = "Measurement",
• volume = "90",
• pages = "526--533",
• year = "2016",
• ISSN = "0263-2241",
• DOI = "doi:10.1016/j.measurement.2016.05.004",
• URL = "http://www.sciencedirect.com/science/article/pii/S0263224116301440",
• 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.",
• 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