Stiffness performance of polyethylene terephthalate modified asphalt mixtures estimation using support vector machine-firefly algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Soltani:2015:Measurement,
  author =       "Mehrtash Soltani and Taher Baghaee Moghaddam and 
                 Mohamed Rehan Karim and Shahaboddin Shamshirband and 
                 Ch Sudheer",
  title =        "Stiffness performance of polyethylene terephthalate
                 modified asphalt mixtures estimation using support
                 vector machine-firefly algorithm",
  journal =      "Measurement",
  volume =       "63",
  pages =        "232--239",
  year =         "2015",
  ISSN =         "0263-2241",
  DOI =          "doi:10.1016/j.measurement.2014.11.022",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263224114005831",
  abstract =     "Predicting asphalt pavement performance is an
                 important matter which can save cost and energy. To
                 ensure an accurate estimation of performance of the
                 mixtures, new soft computing techniques can be used. In
                 this study, in order to estimate the stiffness property
                 of Polyethylene Terephthalate (PET) modified asphalt
                 mixture, different soft computing methods were
                 developed, namely: support vector machine-firefly
                 algorithm (SVM-FFA), genetic programming (GP),
                 artificial neural network (ANN) and support vector
                 machine. The support vector machine-firefly algorithm
                 (SVM-FFA) is a metaheuristic search algorithm developed
                 according to the socially dashing manners of fireflies
                 in nature. To develop the models, experiments were
                 performed. The process, which simulates the mixtures'
                 stiffness, was created with a soft computing method,
                 the inputs being PET percentages, stress levels and
                 environmental temperatures. The performance of the
                 proposed system was confirmed by the simulation
                 results. Soft computing methodologies show very good
                 learning and prediction capabilities and the results
                 obtained in this study indicate that the SVM-FFA
                 contributed the most significant effect on stiffness
                 performance estimation since the SVM-FFA model had a
                 better correlation coefficient than the SVM, ANN and GP
                 approaches. R2 and RMSE were used for making
                 comparisons between the expected and actual values of
                 SVM-FFA, GP, ANN and SVM. The proposed SVM-FFA
                 methodology predicted the output values with 254.4743
                 (mm/day) and 0.9957 RMSE and R2 respectively.",
  keywords =     "genetic algorithms, genetic programming, Firefly
                 algorithm, Support vector machine, Pavement
                 performance, PET modified asphalt mixtures,
                 Environmental conditions, Estimation",
}

Genetic Programming entries for Mehrtash Soltani Taher Baghaee Moghaddam Mohamed Rehan Karim Shahaboddin Shamshirband Sudheer Ch

Citations