The good estimation of pile bearing capacity, derived by total axial pile bearing capacity, can be obtained through numerous empirical, analytical, and field tests. Thus, the application of the methods has been a difficult task due to the uncertainties of various factors related to the properties of soil and rock, which, unlike other engineering materials, are subject to spatial uncertainty. On the other hand, performing field tests such as static and dynamic load test is time-consuming and expensive; hence, finite element and Artificial Neural Networks (ANNs) methods are often of interest. This paper explains the finite element and ANNs methods to estimate the pile bearing capacity of sandy soil. The ANNs method is used to estimate the bearing capacity by using dynamic load test data. The outputs of finite element modeling were compared with a well-established empirical method for estimating the pile’s ultimate axial bearing capacity. The results show that finite element and ANNs prediction on the percentage of the ultimate load is close to each other.

1. R. Nazir, E. Momeni, K. Marsono and H. Maizir, “An artificial neural network approach for prediction of bearing capacity of spread foundations in sand,” Jurnal Teknologi, vol. 72, no. 3, pp. 9-14, 2015.
2. H. Ardalan, A. Eslami and N. Nariman-Zadeh, “Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms,” Computers and Geotechnics, vol. 36, no. 4, pp. 616-625, 2009.
3. W. T. Chan, Y. K. Chow and L. F. Liu, “Neural network: An alternative to pile driving formulas,” Computers and Geotechnics, vol. 17, no. 2, pp. 135-156, 1995.
4. I. M. Lee and J. H. Lee, “Prediction of pile bearing capacity using artificial neural networks,” Computers and Geotechnics, vol. 18, no. 3, pp. 189-200, 1996.
5. C. I. Teh, K. S. Wong, A. T. C. Goh and S. Jaritngam, “Prediction of pile capacity using neural networks,” Journal of Computing in Civil Engineering, vol. 11, no. 2, pp. 129-138, 1997.
6. E. Momeni, H. Maizir, N. Gofar and R. Nazir, “Comparative study on prediction of axial bearing capacity of driven piles in granular materials,” Jurnal Teknologi, vol. 61, no. 3, pp. 15-20, 2013.
7. M. A. Shahin, M. B. Jaksa and H. R. Maier, “Artificial neural network applications in geotechnical engineering,” Australian Geomechanics, vol. 36, no. 1, pp. 49-62, 2001.
8. M. F. Randolph, “Science and empiricism in pile foundation design,” Geotechnique, vol. 53, no. 10, pp. 847-876, 2003.
9. B. H. Fellenius, “The analysis of results from routine pile load tests,” Ground Engineering, vol. 13, no. 6, pp. 19-31, 1980.
10. G. Likins and F. Rausche, “Correlation of CAPWAP with static load test,” in Proceedings of the Seventh International Conference on the Application of Stress wave Theory to Piles 2004, The Institute of Engineers Malaysia, Malaysia, 2004.
11. N. Gofar and M. Angelo, “Evaluation of design capacity of bored piles based on high strain dynamic test,” in Proceedings of the 10th ISGE Annual Science Meeting, Bandung, Indonesia, 2006.

H. Maizir, R. Suryanita and H. Jingga, “Estimation of pile bearing capacity of single driven pile in sandy soil using finite element and artificial neural network methods,” International Journal of Applied and Physical Sciences, vol. 2, no. 2, pp. 45-50, 2016.