Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming

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@Article{Alemdag:2016:EG,
  author =       "S. Alemdag and Z. Gurocak and A. Cevik and 
                 A. F. Cabalar and C. Gokceoglu",
  title =        "Modeling deformation modulus of a stratified
                 sedimentary rock mass using neural network, fuzzy
                 inference and genetic programming",
  journal =      "Engineering Geology",
  volume =       "203",
  pages =        "70--82",
  year =         "2016",
  note =         "Special Issue on Probabilistic and Soft Computing
                 Methods for Engineering Geology",
  ISSN =         "0013-7952",
  DOI =          "doi:10.1016/j.enggeo.2015.12.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0013795215300971",
  abstract =     "This paper investigates a series of experimental
                 results and numerical simulations employed to estimate
                 the deformation modulus of a stratified rock mass. The
                 deformation modulus of rock mass has a significant
                 importance for some applications in engineering geology
                 and geotechnical projects including foundation, slope,
                 and tunnel designs. Deformation modulus of a rock mass
                 can be determined using large scale in-situ tests. This
                 large scale sophisticated in-situ testing equipments
                 are sometimes difficult to install, plus time consuming
                 to be employed in the field. Therefore, this study aims
                 to estimate indirectly the deformation modulus values
                 via empirical methods such as the neural network, neuro
                 fuzzy and genetic programming approaches. A series of
                 analyses have been developed for correlating various
                 relationships between the deformation modulus of rock
                 mass, rock mass rating, rock quality designation,
                 uniaxial compressive strength, and elasticity modulus
                 of intact rock parameters. The performance capacities
                 of proposed models are assessed and found as quite
                 satisfactory. At the completion of a comparative study
                 on the accuracy of models, in the results, it is seen
                 that overall genetic programming models yielded more
                 precise results than neural network and neuro fuzzy
                 models.",
  keywords =     "genetic algorithms, genetic programming, Deformation
                 modulus, Rock mass, Neural network, Neuro fuzzy",
  notes =        "Department of Geological Engineering, Gumushane
                 University, Gumushane 29000, Turkey",
}

Genetic Programming entries for Selcuk Alemdag Z Gurocak Abdulkadir Cevik Ali Firat Cabalar Candan Gokceoglu

Citations