Uplift capacity prediction of suction caisson in clay using a hybrid intelligence method (GMDH-HS)
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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