Forthcoming articles

International Journal of Hydromechatronics

International Journal of Hydromechatronics (IJHM)

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International Journal of Hydromechatronics (1 paper in press)

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  • The effects of particle swarm optimization and genetic algorithm on ANN results in predicting pile bearing capacity   Order a copy of this article
    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