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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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