Effect of Different Factors of Neural Network on Soil Profile of Khartoum State
[1]
H. Elarabi, Building and Road Research Institute, University of Khartoum, Khartoum, Sudan.
[2]
N. F. Taha, Building and Road Research Institute, University of Khartoum, Khartoum, Sudan.
The Artificial Neural Networks (ANNs) is well suited to model complex problems where the relationship between the model variables is unknown. Soil profile information can be increasingly more valuable for decision making when coupled with Artificial Intelligence (AI). In this paper the effect of ANN geometry and some internal parameters on the performance of ANN models was studied by apply the networks for a narrow zone .The influence of these factors appears clearly to affect the predicting results.
Artificial Neural, Soil Profile, Khartoum, Geometry Parameters
[1]
Dr. Chi Leung Patrick Hui (Ed.), Study for Application of Artificial Neural Networks in Geotechnical Problems, Artificial Neural Networks – Application.
[2]
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function, Mathematics of Control Signals and Systems, Vol.2, No.4, pp. 303-314.
[3]
Hornik, K., Stinchcombe, M., and White, H. (1989). "Multilayer feed-forward networks are universal approximators." Neural Networks, 2, 359-366.
[4]
Masters, T. (1993). Practical neural network recipes in C++, Academic Press, SanDiego, California.
[5]
Mohamed A. Shahin1; Mark B. Jaksa2; and Holger R. Maier3,“State of the Art of Artificial Neural Networks in Geotechnical Engineering”, Electronic Journal of Geotechnical Engineering, vol. Special Volume Bouquet 08 (www.ejge.com).
[6]
Mohamed A. Shahin1; Holger R. Maier2; and Mark B. Jaksa3, (2002), “Predicting Settlement of Shallow Foundations using Neural Networks”, Pp: (785-793).
[7]
Shahin, M. A . Maier, H. R. & Jaksa (2000), “Evolutionary data division methods for developing artificial neural network models in geotechnical engineering”, Journal of Geotechnical Engineering - ASCE, Vol.1.
[8]
Maren, A., Harston, C., and Pap, R. (1990). Handbook of neural computing applications, Academic Press, San Diego, California.
[9]
Rojas, R. (1996). Neural networks: A systematic introduction, Springer-Verlag, Berlin.
[10]
Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2004). "Data division for developing neural networks applied to geotechnical engineering." Journal of Computing in Civil Engineering, ASCE, 18(2), 105-114.
