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

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 (Al) 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. (C) 2016 Elsevier Ltd. All rights reserved.