Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network
Created by W.Langdon from
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- @Article{Beiki20101091,
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author = "Morteza Beiki and Ali Bashari and Abbas Majdi",
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title = "Genetic programming approach for estimating the
deformation modulus of rock mass using sensitivity
analysis by neural network",
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journal = "International Journal of Rock Mechanics and Mining
Sciences",
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volume = "47",
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number = "7",
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pages = "1091--1103",
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year = "2010",
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ISSN = "1365-1609",
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DOI = "doi:10.1016/j.ijrmms.2010.07.007",
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URL = "http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4",
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keywords = "genetic algorithms, genetic programming, Deformation
modulus of rock mass, Relative strength of effect
(RSE), Sensitivity analysis about the mean",
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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