Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network

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@Article{Cevik20112587,
  author =       "Abdulkadir Cevik and Ebru {Akcapinar Sezer} and 
                 Ali Firat Cabalar and Candan Gokceoglu",
  title =        "Modeling of the uniaxial compressive strength of some
                 clay-bearing rocks using neural network",
  journal =      "Applied Soft Computing",
  volume =       "11",
  number =       "2",
  pages =        "2587--2594",
  year =         "2011",
  note =         "The Impact of Soft Computing for the Progress of
                 Artificial Intelligence",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2010.10.008",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-51F7PJN-1/2/29835a31bf86c4e457cfa3e0ae15bae5",
  keywords =     "genetic algorithms, genetic programming, Clay-bearing
                 rock, Uniaxial compressive strength, Neural network,
                 Slake durability index",
  abstract =     "Uniaxial compressive strength of intact rock is
                 significantly important for engineering geology and
                 geotechnics, because it is an important design
                 parameter for tunnels, rock slopes rock foundations,
                 and it is also used as input parameter in some rock
                 mass classification systems. This paper documents the
                 results of laboratory experiments and numerical
                 simulations (i.e. neural network) conducted to estimate
                 the uniaxial compressive strength of some clay-bearing
                 rocks selected from Turkey. Emphasis was placed on
                 assessing the role of slake durability indices and clay
                 contents. The input variables in developed neural
                 network (NN) model are the origin of rocks,
                 two/four-cycle slake durability indices and clay
                 contents, and the output is uniaxial compressive
                 strength. It is shown that the performance of
                 capacities of proposed NN model is quite satisfactory.
                 However, the NN model including four cycle slake
                 durability index yielded slightly more precise results
                 than that including two cycle slake durability index as
                 input parameter. The paper also presents a comparative
                 study on the accuracy of NN model and genetic
                 programming (GP) in the results.",
}

Genetic Programming entries for Abdulkadir Cevik Ebru Akcapinar Sezer Ali Firat Cabalar Candan Gokceoglu

Citations