Application of Artificial Neural Network for Prediction of Sudan Soil Profile
[1]
H. Elarabi, Building and Road Research Institute, University of Khartoum, Khartoum, Sudan.
[2]
S. A. Abdelgalil, Building and Road Research Institute, University of Khartoum, Khartoum, Sudan.
The aim of this paper is to predict the natural geotechnical profile of Sudan country, which is very important and vital for all civil engineering work. Availability of pre-predicted profile before performing drilling and boring minimize time and cost. To achieve this goal Artificial Neural Networks (ANNs) program is used. This program has capabilities to study and process the problems that have complex variable, such as Sudan topographical profile. Five main ANN models were constructed based on the soil data of 1909 boreholes from 417 sites. These models use the three dimensional coordinates as input data to predict soil profile and soil parameters at different locations. Artificial Neural Networks is found to have the acceptable ability to predict the soil classification and soil parameters in Sudan. The lack in accuracy in some predicted data when compared with the soil profile obtained from actual boreholes is due to inconsistency of coordinates and depth.
Soil Profile, Artificial Neural Networks, Sudan, Prediction
[1]
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[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]
Elnasr ,S,(2009),“Application of Artificial Neural Networks in Prediction of Soil Profile in Sudan, MSc thesis BBRI, University of Khartoum ,Khartoum, Sudan.
[5]
Ali,M, (2009),“ Prediction of Blue Nile Soil Profile Using Artificial Neural Network”, Paper BBRI, University of Khartoum, Khartoum, Sudan.
[6]
M. A .Shahin , H. R. Maier &Jaksa (2000), “Evolutionary data division methods for developing artificial neural network models in geotechnical engineering .
[7]
Ralf PECK. “Foundation Engineering.” Professeor of Foundation Engineering. University of IIIinois at Urbana- Champaign.
