Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study

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  author =       "B. S. Narendra and P. V. Sivapullaiah and 
                 S. Suresh and S. N. Omkar",
  title =        "Prediction of unconfined compressive strength of soft
                 grounds using computational intelligence techniques: A
                 comparative study",
  journal =      "Computers and Geotechnics",
  year =         "2006",
  volume =       "33",
  number =       "3",
  pages =        "196--208",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Soft ground,
                 Saline soil, Cement stabilisation, Empirical model,
                 Multilayer perceptron, Radial basis function,
                 Unconfined compressive strength",
  DOI =          "doi:10.1016/j.compgeo.2006.03.006",
  abstract =     "Cement stabilisation is one of the commonly used
                 techniques to improve the strength of soft
                 ground/clays, generally found along coastal and low
                 land areas. The strength development in cement
                 stabilization technique depends on the soil properties,
                 cement content, curing period and environmental
                 conditions. For optimal and effective use of cement,
                 there is a need to develop a mathematical model
                 relating the gain in strength in terms of the variables
                 responsible. The existing empirical model in the
                 literature assumes linear variation of normalised
                 strength with the logarithm of curing period and hence,
                 different empirical models are required for different
                 conditions of the same soil. Also, the accuracy of
                 strength prediction is unsatisfactory. Due to unknown
                 functional relationships and nonlinearity in strength
                 development, in this paper the computational
                 intelligence techniques such as multilayer perceptron
                 (MLP), radial basis function (RBF) and genetic
                 programming (GP) are used to develop a mathematical
                 model. To generate the mathematical model, an
                 experimental study is conducted to obtain the strength
                 of three inland soils namely, red earth (CL), brown
                 earth (CH) and black cotton soil (CH) for different
                 water contents, cement contents and curing periods. In
                 order to generate a generic mathematical model using
                 computational intelligence techniques, two saline soils
                 (Ariake clay-3 and Ariake clay-4) and three inland
                 soils are used. A detailed study of the relative
                 performance of the computational intelligence
                 techniques and the empirical model has been carried

Genetic Programming entries for B S Narendra P V Sivapullaiah Sundaram Suresh S N Omkar