Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7123678 | Measurement | 2016 | 8 Pages |
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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Authors
Taher Baghaee Moghaddam, Mehrtash Soltani, Hamed Shahrokhi Shahraki, Shahaboddin Shamshirband, Noorzaily Bin Mohamed Noor, Mohamed Rehan Karim,