Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks
Science Research
Volume 2, Issue 3, June 2014, Pages: 43-48
Received: May 22, 2014; Accepted: Jun. 9, 2014; Published: Jun. 20, 2014
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Authors
Hussein Elarabi, Geotechnical Department, Building and Road Research Institute, University of Khartoum, Sudan
Nahed F. Taha, Building and Road Research Institute, University of Khartoum, Sudan
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Abstract
Artificial neural networks (ANNs) are a form of artificial intelligence (AI), which in their architecture attempt to simulate the biological structure of the human brain and nervous system. In this report, back-propagation neural networks are used to predict soil classification and soil parameters of Khartoum State. The study was based on the available data collected from specified areas in Khartoum, and then the results were compared with data brought from actual boreholes to check the ANN model validity. The results indicate that artificial neural networks are a promising method in predicting soil classification and soil parameters of Khartoum State.
Keywords
Artificial Neural Networks, Soil Profile, Soil Parameters, Khartoum, Sudan
To cite this article
Hussein Elarabi, Nahed F. Taha, Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks, Science Research. Vol. 2, No. 3, 2014, pp. 43-48. doi: 10.11648/j.sr.20140203.13
References
[1]
El Hassan, M., (2009), “Prediction of Blue Nile Soil Profile Using Artificial Neural Network”, M. Sc. thesis BBRI, Uni-versity of Khartoum, Khartoum, Sudan.
[2]
Mohammed, S. Elnasr (2009), “APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN PREDICTION OF SOIL PROFILE IN SUDAN, MSc thesis BBRI, University of Khartoum, Khartoum, Su-dan.
[3]
Mohamed A. Shahin1; Holger R. Maier2; and Mark B. Jaksa3, (2002), “Predicting Settlement of Shallow Foundations using Neural Networks”, Pp: (785-793).
[4]
Mohamed, K.M. (2005), “Artificial Intelligence Applica-tions in Geotechnical Engineering in Sudan”, MSc thesis, BBRI, University of Khartoum, Khartoum, Su-dan.
[5]
Nour Alfadul, Y.M. (2007), “Soil Profile Prediction Using Artificial Neural Networks in Sudan”, MSc thesis BBRI, University of Khartoum, Khartoum, Sudan.
[6]
Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). "Artifi-cial neural network applications in geotechnical engineering." Australia Geomechanics, 36(1), 49-62.
[7]
M. A .Shahin, H. R. Maier &Jaksa (2000), “Evolutionary data division methods for developing artificial neural network models in geotechnical engineering” Journal of Geotechnical Engineering - ASCE, Vol.1.
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