The effects of particle swarm optimization and genetic algorithm on ANN results in predicting pile bearing capacity

Abstract

Pile as a foundation type has transferred the heavy structural loads into the ground. Proper prediction and determining of pile bearing capacity has been signified in initial geotechnical structures designing. The current study has attempted to build two hybrid intelligent models for pile bearing capacity prediction. Presenting the influence of genetic algorithm (GA) and particle swarm optimisation (PSO) on a pre-developed artificial neural network (ANN), two hybrid models, i.e., GA-ANN and PSO-ANN have been built to pile bearing capacity prediction. To do this, an established database in literature has been used to develop intelligent systems. Then, the most important parameters on GA and PSO have been designed to optimise ANN weights and biases for receiving the best results. Then, the best predictive models of GA-ANN and PSO-ANN were selected based on three performance indices, i.e., R2, RMSE and VAF. Respectively, R2 variables as (0.975 and 0.988) and (0.985 and 0.993) have been gained to train and test of datasets in GA-ANN and PSO-ANN. The outcomes have proved both hybrid methods as capable with highly accurate bearing capacity prediction, however, PSO-ANN predictive model is more applicable in terms of performance capacity and it can be introduced as a new technique in this field.