Applying several soft computing techniques for prediction of bearing capacity of driven piles

Abstract

Pile as a type of foundation is a structure which can transfer heavy structural loads into the ground. Determination and proper prediction of pile bearing capacity are considered as a very important issue in preliminary design of geotechnical structures. This study attempts to develop several intelligent techniques for prediction of pile bearing capacity in cohesionless soil. To show the effects of fuzzy inference system and imperialism competitive algorithm (ICA) on a pre-developed artificial neural network (ANN), two hybrid ANN models namely ICA-ANN and adoptive neuro-fuzzy inference system (ANFIS) were considered and developed to estimate pile bearing capacity. Then, results of these techniques were compared with those of ANN model and the best one among them was chosen according to the results of performance indices. Several parameters (i.e., internal friction angle of soil located in shaft and tip, effective vertical stress at pile toe, pile area, and pile length) were set as model inputs, while the output is the total driven pile bearing capacity. As a result of the developed models, coefficient of determination (R2) values of (0.895, 0.905), (0.945, 0.958), and (0.967, 0.975) were obtained for training and testing data sets of ANN, ICA-ANN, and ANFIS models, respectively. The results showed that both hybrid models are able to predict bearing capacity with high degree of accuracy; however, ANFIS receives more applicable based on used performance indices and it can be utilized for further researchers and engineers in practice.