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International Journal of Hydromechatronics (1 paper in press)
The effects of particle swarm optimization and genetic algorithm on ANN results in predicting pile bearing capacity by Bhatawdekar Ramesh Murlidhar, Rabindra Kumar Sinha, Edy Tonnizam Mohamad, Rajesh Sonkar, Majid Khorami Abstract: The current study has attempted to build 2 hybrid intelligent models for pile bearing capacity prediction Presenting the influence of genetic algorithm (GA) and particle swarm optimization (PSO) on a pre-developed artificial neural network (ANN), 2 hybrid models i e , GA-ANN and PSO-ANN have been built to pile bearing capacity prediction Then, the best predictive models of GA-ANN and PSO-ANN were selected based on 3 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. Keywords: Piling; Ultimate bearing capacity; Genetic algorithm; Particle swarm optimization; Artificial neural network. DOI: 10.1504/IJHM.2019.10023991