Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@Article{journals/ewc/KhandelwalFMAMY17,
  author =       "Manoj Khandelwal and Roohollah Shirani Faradonbeh and 
                 Masoud Monjezi and Danial Jahed Armaghani and 
                 Muhd Zaimi Bin Abd. Majid and Saffet Yagiz",
  title =        "Function development for appraising brittleness of
                 intact rocks using genetic programming and non-linear
                 multiple regression models",
  journal =      "Engineering with Computers",
  year =         "2017",
  volume =       "33",
  number =       "1",
  month =        jan,
  pages =        "13--21",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0177-0667",
  bibdate =      "2017-06-06",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ewc/ewc33.html#KhandelwalFMAMY17",
  DOI =          "doi:10.1007/s00366-016-0452-3",
  abstract =     "Brittleness of rock is one of the most critical
                 features for design of underground excavation project.
                 Therefore, proper assessing of rock brittleness can be
                 very useful for designers and evaluators of
                 geotechnical applications. In this study, feasibility
                 of genetic programming (GP) model and non-linear
                 multiple regression (NLMR) in predicting brittleness of
                 intact rocks is examined. For this purpose, a dataset
                 developed by conducting various rock tests including
                 uniaxial compressive strength, Brazilian tensile
                 strength, unit weight and brittleness via punch
                 penetration on rock samples gathered from 48 tunnels
                 projects around the world is used herein. Considering
                 multiple inputs, several GP models were constructed to
                 estimate brittleness index of the rock and finally, the
                 best GP model was selected. Note that, GP can make an
                 equation for predicting output of the system using
                 model inputs. To show applicability of the developed GP
                 model, non-linear multiple regression (NLMR) was also
                 applied and developed. Considering some model
                 performance indices, performance prediction of the GP
                 and NLMR models were evaluated and it was found that
                 the GP model is superior to NLMR one. Based on
                 coefficient of determination (R2) of testing datasets,
                 by proposing GP model, it can be improved from 0.882
                 (obtained by NLMR model) to 0.904. It is worth
                 mentioning that the proposed predictive models in this
                 study should be planned and used for the similar types
                 of rock and the established inputs ranges.",
  notes =        "Eng. Comput. (Lond.)",
}

Genetic Programming entries for Manoj Khandelwal Roohollah Shirani Faradonbeh Masoud Monjezi Danial Jahed Armaghani Muhd Zaimi Bin Abd Majid Saffet Yagiz

Citations