Comparison of two Different Application of Neural Network on Sudan Soil Profile
Science Innovation
Volume 2, Issue 5-1, September 2014, Pages: 1-4
Received: May 8, 2014; Accepted: Jun. 23, 2014; Published: Jul. 7, 2014
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Authors
Hussien Elarabi, Building and Road Research Institute, University of Khartoum, Khartoum, Sudan
Safa A. Abdelgalil, Building and Road Research Institute, University of Khartoum, Khartoum, Sudan
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Abstract
The objective of this paper is to compare between two studies that had been carried out using ANNs in prediction of Sudan soil profile. This importance in the design and implementation of all engineering projects which reduce cost and time. Artificial Neural Networks (ANNs) program is applied to realize this aim. The data of 1909 boreholes from 417 sites was used firstly for a single model and then divided to five zones to be used for localized models. The input data is the coordinate and depth and the output data is the soil classification and soil parameters. The result showed that ANNs can be used as a good decision support and source of information for soils profiles. It is more efficient tools to be used for small zones than all area.
Keywords
Soil Profile, Artificial Neural Networks, Sudan, Prediction
To cite this article
Hussien Elarabi, Safa A. Abdelgalil, Comparison of two Different Application of Neural Network on Sudan Soil Profile, Science Innovation. Special Issue:Innovation Sciences--Managing Technology in Society. Vol. 2, No. 5-1, 2014, pp. 1-4. doi: 10.11648/j.si.s.2014020501.11
References
[1]
Elarabi, H.& Ali, K., “Soil Classification Modeling using Artificial Neural Network”, the International Conference on Intelligent Systems ( ICIS2009), Kingdom of Bahrain, Dec 2008.
[2]
Mustafa, K(2005),“Artificial Intelligence Applications in Geotechnical Engineering in Sudan”, MSc thesis, BBRI, University of Khartoum ,Khartoum, Sudan.
[3]
Elarabi, H.; Abbas, Y. “Soil Profile Prediction in Khartoum Using Artificial Neural Networks”, 4th African Regional Conference on Soil Mechanics an Geotechnical Nov. 2007
[4]
Mohammed, Y ,(2007),“Soil Profile Prediction Using Artificial Neural Networks in Sudan” , MSc thesis BBRI, University of Khartoum, Khartoum, Sudan.
[5]
Elnasr ,S,(2009),“Application of Artificial Neural Networks in Prediction of Soil Profile in Sudan, MSc thesis BBRI, University of Khartoum ,Khartoum, Sudan.
[6]
Ali,M, (2009),“ Prediction of Blue Nile Soil Profile Using Artificial Neural Network”, Paper BBRI,University of Khartoum,Khartoum,Sudan.
[7]
M. A .Shahin , H. R. Maier &Jaksa (2000), “Evolutionary data division methods for developing artificial neural network models in geotechnical engineering.
[8]
Ralf PECK. “Foundation Engineering.” Professeor of Foundation Engineering. University of IIIinois at Urbana- Champaign.
[9]
Jaksa, M. B. (1995). “The influence of spatial variability on the geotechncial design properties of a stiff, over consolidated clay,” PhD thesis, The University of Adelaide, Adelaide.
[10]
Hecht-Nielsen, R. (1990) Neurocomputing, Reading, MA: Addison-Wesley.
[11]
Maren, A. J., Harston, C. T. and Pap, R. M. 1990. Handbook of Neural Computing Applications, Academic Press, San Diego, Calif.
[12]
Fausett, L. V., 1994, Fundamentals of neural networks: Architecture, algorithms, and applications, Prentice-Hall, Englewood Cliffs, N.J.
[13]
Ripley, B. D. and Hjort, N. L. (1996) Pattern Recognition and Neural Networks - A Statistical Approach. Cambridge Universityh Press.
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