The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures |
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| Affiliation: | 1. Centre for Pavement and Transportation Technology, Department of Civil and Environmental Engineering, Faculty of Engineering, University of Waterloo, Waterloo N2L 3G1, Canada;2. Center for Transportation Research, Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;3. Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;4. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada;1. Department of Civil Engineering, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa;2. Ove Arup & Partners Limited, 1st Floor City Gate West, Tollhouse Hill, Nottingham NG1 5AT, United Kingdom;3. JG Afrika (Pty) Ltd, JG Afrika House, 37 Sunninghill Office Park, Peltier Drive, Sunninghill 2191, South Africa;1. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;2. Graduate School of Tangshan, Southwest Jiaotong University, Tangshan 063000, China;3. Highway Engineering Laboratory of Sichuan Province, Chengdu, Sichuan 610031, China;4. Sichuan Yakang Expressway CO., LTD, Chengdu 610000, China;1. Faculty of Civil Engineering, University of Science and Technology of Mazandaran, Behshahr P.O.Box 48518-78195, Iran;2. Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran |
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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: | Firefly algorithm Support vector machine PET modified asphalt mixtures Environmental conditions Fatigue life |
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