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

The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Measurement Elsevier

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

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
0263-2241
eISSN
1873-412X
D.O.I.
10.1016/j.measurement.2016.05.004
Publisher site
See Article on Publisher Site

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.

Journal

MeasurementElsevier

Published: Aug 1, 2016

References

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