Prediction of SWCC using artificial intelligent systems: A comparative study

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "A. Johari and G. Habibagahi and A. Ghahramani",
  title =        "Prediction of SWCC using artificial intelligent
                 systems: A comparative study",
  journal =      "Scientia Iranica",
  volume =       "18",
  number =       "5",
  pages =        "1002--1008",
  year =         "2011",
  ISSN =         "1026-3098",
  DOI =          "doi:10.1016/j.scient.2011.09.002",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Unsaturated
                 soils, Soil suction, Soil Water Characteristic Curve
                 (SWCC), Geotechnical models, Computer models, Numerical
  abstract =     "The significance of the Soil Water Characteristic
                 Curve (SWCC) or soil retention curve in understanding
                 the unsaturated soils behaviour such as shear strength,
                 volume change and permeability has resulted in many
                 attempts for its prediction. In this regard, the
                 authors had previously developed two models, namely.
                 Genetic-Based Neural Network (GBNN) and Genetic
                 Programming (GP). These two models have identical set
                 of input parameters. These parameters include void
                 ratio, initial water content, clay fraction, silt
                 content and logarithm of suction normalised with
                 respect to air pressure. In this paper, performance of
                 these two models is further investigated using
                 additional test data. For this purpose, soil samples
                 from 14 different locations in Shiraz city in the Fars
                 province of Iran are tested and their SWCCs are
                 established, using a pressure plate apparatus. Next,
                 the results are used to demonstrate the suitability of
                 the previously proposed models and to evaluate relative
                 importance of the input parameters. Assessment of the
                 results indicates that predictions from GBNN model have
                 relatively higher accuracy as compared to GP model.",

Genetic Programming entries for A Johari Ghassem Habibagahi Arsalan Ghahramani