Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil

The application of models provided by artificial neural network (ANN) in predicting bearing capacity of driven pile is underlined in several investigations. However, weakness of ANN in slow rate of convergence as well as finding reliable testing output is known to be the major drawbacks of implementing ANN-based techniques. The present study aims to introduce and evaluate an optimized ANN with imperialism competitive algorithm (ICA) model based to estimate bearing capacity of driven pile in cohesionless soil. The training data for optimizing the ICA-ANN structure are based on the in situ study. To develop the ICA-ANN model, the input parameters are internal friction angle of soil located in shaft (φ shaft), and tip (φ tip), pile length (L), effective vertical stress at pile toe (σv), and pile area (A) while the output is the total driven pile bearing capacity in cohesionless soil. The predicted results are compared with a pre-developed ANN model to demonstrate the ability of the hybrid model. As a result, coefficient of determination (R2) values of (0.885 and 0.894) and (0.964 and 0.974) was obtained for testing and training datasets of ANN and ICA-ANN models, respectively. In addition, values of variance account for (VAF) of (88.212 for training and 89.215 for testing) and (96.369 for training and 97.369 for testing, respectively) were obtained for ANN and ICA-ANN models, respectively. The obtained results declare high reliability of the developed ICA-ANN model. This model can be introduced as a new model in field of deep foundation engineering.