Evaluation of Rutting Potential of Asphalt Mixtures Using Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Mirzahosseini:2010:ISAP,
  author =       "Mohammadreza R. Mirzahosseini and 
                 Amirhossein H. Alavi and Fereidoon {Moghadas Nejad} and 
                 Amirhossein H. Gandomi and Mahmoud Ameri",
  title =        "Evaluation of Rutting Potential of Asphalt Mixtures
                 Using Linear Genetic Programming",
  booktitle =    "The 11th International Conference on Asphalt Pavements
                 (ISAP 2010)",
  year =         "2010",
  volume =       "2",
  pages =        "1527--1536?",
  address =      "Nagoya, Japan",
  publisher_address = "57 Morehouse Lane Red Hook, NY 12571 USA Phone:
                 845-758-0400 Fax: 845-758-2634 Email:
                 curran@proceedings.com",
  month =        "1-6 " # aug,
  organisation = "International Society for Asphalt Pavements (ISAP)
                 6776 Lake Drive, Suite 215 Lino Lakes, MN 55014",
  publisher =    "Curran Associates, Inc. (Sep 2011)",
  keywords =     "genetic algorithms, genetic programming, Rutting, Flow
                 number, Linear genetic programming, Regression
                 Analysis, Marshall mix design",
  isbn13 =       "978-1-61839-073-8",
  URL =          "http://www.proceedings.com/12470.html",
  URL =          "https://www.researchgate.net/publication/236619155_Evaluation_of_Rutting_Potential_of_Asphalt_Mixtures_Using_Linear_Genetic_Programming",
  size =         "10 pages",
  abstract =     "Rutting has been considered as the most serious
                 distresses in flexible pavement for many years. Flow
                 number obtained from uniaxial dynamic creep test is an
                 explanatory index for the evaluation of rutting
                 potential of asphalt mixtures. This is a pioneer study
                 that presents a promising variant of genetic
                 programming, namely linear genetic programming (LGP) to
                 predict the flow number of dense asphalt-aggregate
                 mixtures. Generalized LGP-based models were constructed
                 to relate the flow number of Marshall specimens to the
                 coarse and fine aggregate contents, percentage of air
                 voids, percentage of voids in mineral aggregate,
                 Marshall stability and flow. The comprehensive
                 experimental database used for the development of the
                 models was established upon a series of uniaxial
                 dynamic creep tests conducted in this study. The
                 contributions of the parameters affecting the flow
                 number were determined through a sensitivity analysis.
                 A multiple least squares regression (MLSR) analysis was
                 performed using the same variables and same data sets
                 to benchmark the LGP models. For more verification, a
                 subsequent parametric study was conducted and the
                 trends of the results were confirmed with the results
                 of previous studies. The results indicate that the
                 proposed LGP models are capable of effectively
                 evaluating the flow number of asphalt mixtures. The LGP
                 models are found to be significantly more accurate than
                 the MLSR model.",
  notes =        "Paper ID.
                 90316

                 http://www.gbv.de/dms/tib-ub-hannover/653947240.pdf
                 gives pages as Volume II, 211--220?",
}

Genetic Programming entries for Mohammad Reza Mirzahosseini A H Alavi Fereidoon Moghaddas Nejad A H Gandomi Mahmoud Ameri

Citations