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  Volume 1, Number 2, December 2020 , pages 165-186
 open access

Pile bearing capacity estimation using GEP, RBFNN and MVNR techniques
Hooman Harandizadeh, Danial Jahed Armaghani and Vahid Toufigh

    This study introduced a new predictive approach for estimating the bearing capacity of driven piles. To this end, the required data based on literature such as hammer strikes, soil properties, geometry of the pile, and friction angle between pile and soil were gathered as a suitable database. Then, three predictive models i.e., gene expression programming (GEP), radial basis function type neural networks (RBFNN) and multivariate nonlinear regression (MVNR) were applied and developed for pile bearing capacity prediction. After proposing new models, their performance indices i.e., root mean square error (RMSE) and coefficient of determination (R2) were calculated and compared to each other in order to select the best one among them. The obtained results indicated that the RBFNN model is able to provide higher performance prediction level in comparison with other predictive techniques. In terms of R2, results of 0.9976, 0.9466 and 0.831 were obtained for RBFNN, GEP and MVNR models respectively, which confirmed that, the developed RBFNN model could be selected as a new model in piling technology. Definitely, other researchers and engineers can utilize the procedure and results of this study in order to get better design of driven piles.
Key Words
    pile bearing capacity; radial basis function type artificial neural networks (RBFNN); multivariate nonlinear regression (MVNR); gene expression programming (GEP)
Hooman Harandizadeh:Department of Civil Engineering. Faculty of Engineering. Shahid Bahonar University of Kerman.
Pajoohesh Sq. Imam Khomeni Highway. P.O. Box 76169133. Kerman, Iran
Danial Jahed Armaghani:Department of Civil Engineering. Faculty of Engineering. University of Malaya.
50603. Kuala Lumpur, Malaysia
Vahid Toufigh:Faculty of Civil and Surveying Engineering. Graduate University of Advanced Technology. Kerman, Iran

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