A comparison of three forecasting methods to establish a flexible pavement serviceability index

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@InProceedings{Hung:2010:IEEM,
  author =       "Ching-Tsung Hung and Shih-Huang Chen",
  title =        "A comparison of three forecasting methods to establish
                 a flexible pavement serviceability index",
  booktitle =    "2010 IEEE International Conference on Industrial
                 Engineering and Engineering Management (IEEM)",
  year =         "2010",
  month =        dec,
  pages =        "926--929",
  abstract =     "Since 1960, the pavement serviceability index has
                 supported the efforts of engineers who make decisions
                 concerning maintenance strategies. The data of pavement
                 surfaces do not belong to a normal distribution.
                 Because the data violate the basic assumptions of
                 linear regression, the pavement serviceability index is
                 not suitable for regression modelling. Many kinds of
                 prediction models with non-statistical foundations have
                 been developed in recent years. To establish a flexible
                 pavement serviceability index, this paper considers a
                 fuzzy regression model, a support vector machine and a
                 genetic programming. Our support vector machine has the
                 highest predictive accuracy of the three methods in
                 this study. The support vector machine uses a
                 hyperplane transform to process interactions among
                 pavement variables.",
  keywords =     "genetic algorithms, genetic programming, flexible
                 pavement serviceability index, forecasting method,
                 fuzzy regression model, hyperplane transform, linear
                 regression, maintenance strategy, normal distribution,
                 pavement surfaces data, regression modeling, support
                 vector machine, fuzzy set theory, maintenance
                 engineering, normal distribution, regression analysis,
                 roads, structural engineering, support vector
                 machines",
  DOI =          "doi:10.1109/IEEM.2010.5674216",
  ISSN =         "2157-3611",
  notes =        "Also known as \cite{5674216}",
}

Genetic Programming entries for Ching-Tsung Hung Shih-Huang Chen

Citations