Prediction of Pile Bearing Capacity Using Artificial Neural Networks.
Elgamal, A., Elnimr, A., Ahmed Dif, A., Gabr, A. (2021). Prediction of Pile Bearing Capacity Using Artificial Neural Networks.. EKB Journal Management System, 37(3), 1-14. doi: 10.21608/bfemu.2021.156943
A. Elgamal; A. Elnimr; Adel Ahmed Dif; A. Gabr. "Prediction of Pile Bearing Capacity Using Artificial Neural Networks.". EKB Journal Management System, 37, 3, 2021, 1-14. doi: 10.21608/bfemu.2021.156943
Elgamal, A., Elnimr, A., Ahmed Dif, A., Gabr, A. (2021). 'Prediction of Pile Bearing Capacity Using Artificial Neural Networks.', EKB Journal Management System, 37(3), pp. 1-14. doi: 10.21608/bfemu.2021.156943
Elgamal, A., Elnimr, A., Ahmed Dif, A., Gabr, A. Prediction of Pile Bearing Capacity Using Artificial Neural Networks.. EKB Journal Management System, 2021; 37(3): 1-14. doi: 10.21608/bfemu.2021.156943

Prediction of Pile Bearing Capacity Using Artificial Neural Networks.

MEJ- Mansoura Engineering Journal
Article 4, Volume 37, Issue 3, September 2012, Page 1-14  XML PDF (450.69 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.156943
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Authors
A. Elgamal* 1; A. Elnimr2; Adel Ahmed Dif2; A. Gabr3
1Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
2Professor of Structural Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
3Assistant Professor of Geotechnical Structural Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura.,Egypt.
Abstract
It is well known that the human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation neural networks were utilized to predict the working bearing capacity of piles. The data of performed pile load   tests are used to verify the applicability of the presented neural network procedure. The results showed that the maximum error of prediction did not exceed 25%. Thus, the use of Neural Networks to predict pile capacity seems to be feasible for practical purpose. 
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