Uplift capacity prediction of suction caisson in clay using a hybrid intelligence method (GMDH-HS)

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

@Article{MasoumiShahrBabak:2016:AOR,
  author =       "Mojtaba Masoumi Shahr-Babak and 
                 Mohammad Javad Khanjani and Kourosh Qaderi",
  title =        "Uplift capacity prediction of suction caisson in clay
                 using a hybrid intelligence method (GMDH-HS)",
  journal =      "Applied Ocean Research",
  volume =       "59",
  pages =        "408--416",
  year =         "2016",
  ISSN =         "0141-1187",
  DOI =          "doi:10.1016/j.apor.2016.07.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0141118716302450",
  abstract =     "Suction caissons are widely used for offshore
                 facilities foundation or anchor system. They should be
                 very stable and also to provide stability of main
                 massive structures those are upon them. Suction caisson
                 uplift capacity is the main issue to determine their
                 stability. During recent years, many artificial
                 intelligence (AI) methods such as artificial neural
                 network (ANN), genetic programming (GP) and
                 multivariate adaptive regression spline (MARS) have
                 been used for suction caisson uplift capacity
                 prediction. In this study, a novel hybrid intelligent
                 method based on combination of group method of data
                 handling (GMDH) and harmony search (HS) optimization
                 method which is called GMDH-HS has been developed for
                 suction caisson uplift capacity prediction. At first,
                 the Mackey-Glass time series data were used for
                 validation of developed method. The results of
                 Mackey-Glass modeling were compared to conventional
                 GMDH with two kinds of transfer function called GMDH1
                 and GMDH2. Five statistical indices such as coefficient
                 of efficiency (CE), root mean square Error (RMSE), mean
                 square relative error (MSRE), mean absolute percentage
                 error (MAPE) and relative bias (RB) were used to
                 evaluate performance of applied method. Then the
                 GMDH-HS method has been used for suction caisson uplift
                 capacity prediction. The 62 data set of laboratory
                 measurements were collected from published literature
                 that 51 sets used to train new developed method and the
                 remaining data set used for testing. Not only the
                 results of suction caisson uplift capacity prediction
                 using GMDH-HS were evaluated with statistical indices,
                 but also the results were compared to some artificial
                 methods by previously works. The results indicated that
                 performance of GMDH-HS was found more efficient when
                 compared to other applied method in predicting the
                 suction caisson uplift capacity.",
  keywords =     "genetic algorithms, genetic programming, Uplift
                 capacity, Suction caisson, GMDH, GMDH-HS, Prediction,
                 Hybrid intelligent method",
}

Genetic Programming entries for Mojtaba Masoumi Shahr-Babak Mohammad Javad Khanjani Kourosh Qaderi

Citations