Estimating the unconfined compressive strength of carbonate rocks using gene expression programming

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

@Misc{Dindarloo:2016:ArXiv,
  author =       "Saeid R. Dindarloo and Elnaz Siami-Irdemoosa",
  title =        "Estimating the unconfined compressive strength of
                 carbonate rocks using gene expression programming",
  howpublished = "ArXiv",
  year =         "2016",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  bibdate =      "2016-03-01",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/corr/corr1602.html#DindarlooS16",
  URL =          "http://arxiv.org/abs/1602.03854",
  abstract =     "Conventionally, many researchers have used both
                 regression and black box techniques to estimate the
                 unconfined compressive strength (UCS) of different
                 rocks. The advantage of the regression approach is that
                 it can be used to render a functional relationship
                 between the predictive rock indices and its UCS. The
                 advantage of the black box techniques is in rendering
                 more accurate predictions. Gene expression programming
                 (GEP) is proposed, in this study, as a robust
                 mathematical alternative for predicting the UCS of
                 carbonate rocks. The two parameters of total porosity
                 and P-wave speed were selected as predictive indices.
                 The proposed GEP model had the advantage of the both
                 traditionally used approaches by proposing a
                 mathematical model, similar to a regression, while
                 keeping the prediction errors as low as the black box
                 methods. The GEP outperformed both artificial neural
                 networks and support vector machines in terms of
                 yielding more accurate estimates of UCS. Both the
                 porosity and the P-wave velocity were sufficient
                 predictive indices for estimating the UCS of the
                 carbonate rocks in this study. Nearly, 95percent of the
                 observed variation in the UCS values was explained by
                 these two parameters (i.e., R2 =0.95).",
}

Genetic Programming entries for Saeid R Dindarloo Elnaz Siami-Irdemoosa

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