A comparison of three forecasting methods to establish a flexible pavement serviceability index
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- @InProceedings{Hung:2010:IEEM,
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author = "Ching-Tsung Hung and Shih-Huang Chen",
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title = "A comparison of three forecasting methods to establish
a flexible pavement serviceability index",
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booktitle = "2010 IEEE International Conference on Industrial
Engineering and Engineering Management (IEEM)",
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year = "2010",
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month = dec,
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pages = "926--929",
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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.",
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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",
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DOI = "doi:10.1109/IEEM.2010.5674216",
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ISSN = "2157-3611",
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notes = "Also known as \cite{5674216}",
- }
Genetic Programming entries for
Ching-Tsung Hung
Shih-Huang Chen
Citations