Predicting pile dynamic capacity via application of an evolutionary algorithm

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

@Article{Alkroosh:2014:SF,
  author =       "I. Alkroosh and H. Nikraz",
  title =        "Predicting pile dynamic capacity via application of an
                 evolutionary algorithm",
  journal =      "Soils and Foundations",
  volume =       "54",
  number =       "2",
  pages =        "233--242",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISSN =         "0038-0806",
  DOI =          "doi:10.1016/j.sandf.2014.02.013",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0038080614000213",
  size =         "10 pages",
  abstract =     "This study presents the development of a new model
                 obtained from the correlation of dynamic input and SPT
                 data with pile capacity. An evolutionary algorithm,
                 gene expression programming (GEP), was used for
                 modelling the correlation. The data used for model
                 development comprised 24 cases obtained from existing
                 literature. The modelling was carried out by dividing
                 the data into two sets: a training set for model
                 calibration and a validation set for verifying the
                 generalisation capability of the model. The performance
                 of the model was evaluated by comparing its predictions
                 of pile capacity with experimental data and with
                 predictions of pile capacity by two commonly used
                 traditional methods and the artificial neural networks
                 (ANNs) model. It was found that the model performs well
                 with a coefficient of determination, mean, standard
                 deviation and probability density at 50percent
                 equivalent to 0.94, 1.08, 0.14, and 1.05, respectively,
                 for the training set, and 0.96, 0.95, 0.13, and 0.93,
                 respectively, for the validation set. The low values of
                 the calculated mean squared error and mean absolute
                 error indicated that the model is accurate in
                 predicting pile capacity. The results of comparison
                 also showed that the model predicted pile capacity more
                 accurately than traditional methods including the ANNs
                 model.",
  notes =        "The Japanese Geotechnical Society

                 also known as \cite{Alkroosh2014233}

                 Department of Civil Engineering",
  bibsource =    "OAI-PMH server at espace.library.curtin.edu.au",
  oai =          "oai:espace.library.curtin.edu.au:237657",
}

Genetic Programming entries for Iyad Salim Jabor Alkroosh H Nikraz

Citations