Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Beiki20101091,
  author =       "Morteza Beiki and Ali Bashari and Abbas Majdi",
  title =        "Genetic programming approach for estimating the
                 deformation modulus of rock mass using sensitivity
                 analysis by neural network",
  journal =      "International Journal of Rock Mechanics and Mining
                 Sciences",
  volume =       "47",
  number =       "7",
  pages =        "1091--1103",
  year =         "2010",
  ISSN =         "1365-1609",
  DOI =          "doi:10.1016/j.ijrmms.2010.07.007",
  URL =          "http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4",
  keywords =     "genetic algorithms, genetic programming, Deformation
                 modulus of rock mass, Relative strength of effect
                 (RSE), Sensitivity analysis about the mean",
  abstract =     "We use genetic programming (GP) to determine the
                 deformation modulus of rock masses. A database of 150
                 data sets, including modulus of elasticity of intact
                 rock (Ei), uniaxial compressive strength (UCS), rock
                 mass quality designation (RQD), the number of joint per
                 meter (J/m), porosity, and dry density for possible
                 input parameters, and the modulus deformation of the
                 rock mass determined by a plate loading test for
                 output, was established. The values of geological
                 strength index (GSI) system were also determined for
                 all sites and considered as another input parameter.
                 Sensitivity analyses are considered to find out the
                 important parameters for predicting of the deformation
                 modulus of rock mass. Two approaches of sensitivity
                 analyses, based on statistical analysis of RSE values
                 and sensitivity analysis about the mean, are performed.
                 Evolution of the sensitivity analyses results establish
                 the fact that variable of UCS, GSI, and RQD play more
                 prominent roles for predicting modulus of the rock
                 mass, and so those are considered as the predictors to
                 design the GP model. Finally, two equations were
                 achieved by GP. The statistical measures of root mean
                 square error (RMSE) and variance account for (VAF) have
                 been used to compare GP models with the well-known
                 existing empirical equations proposed for predicting
                 the deformation modulus. These performance criteria
                 proved that the GP models give higher predictions over
                 existing empirical models.",
}

Genetic Programming entries for Morteza Beiki Ali Bashari Abbas Majdi

Citations