| Peer-Reviewed

Comparison of Different Methods of Application of Neural Network on Soil Profile of Khartoum State

Received: 7 May 2014     Accepted: 23 May 2014     Published: 30 May 2014
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Abstract

Within the last years, four methods have been developed to predict the soil profile and its parameters in Sudan. However, a method making such predictions with the required degree of accuracy and consistency has not yet been developed. In this paper, artificial neural networks, ANNs are used in an attempt to compare between these methods by applying them on large zone contains many sites to select a unified method. A large database of actual measured is used to develop and verify the ANN model. The predicted soil profile found by utilizing ANNs is compared between them. The results indicate that ANNs are a useful technique for predicting the soil profile and its parameters when using anyone of the compared methods.

Published in International Journal of Science, Technology and Society (Volume 2, Issue 3)
DOI 10.11648/j.ijsts.20140203.15
Page(s) 59-62
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Artificial Neural Network, Soil Profile, Khartoum, Prediction

References
[1] El Hassan, M., (2009),“ Prediction of Blue Nile Soil Profile Using Artificial Neural Network”, M. Sc. thesis BBRI, University 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, Sudan.
[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, Sudan.
[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). "Artificial neural network applications in geotechnical engineer-ing." Australia Geomechanics, 36(1), 49-62.
Cite This Article
  • APA Style

    Hussein Elarabi, N. F. Taha. (2014). Comparison of Different Methods of Application of Neural Network on Soil Profile of Khartoum State. International Journal of Science, Technology and Society, 2(3), 59-62. https://doi.org/10.11648/j.ijsts.20140203.15

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    ACS Style

    Hussein Elarabi; N. F. Taha. Comparison of Different Methods of Application of Neural Network on Soil Profile of Khartoum State. Int. J. Sci. Technol. Soc. 2014, 2(3), 59-62. doi: 10.11648/j.ijsts.20140203.15

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    AMA Style

    Hussein Elarabi, N. F. Taha. Comparison of Different Methods of Application of Neural Network on Soil Profile of Khartoum State. Int J Sci Technol Soc. 2014;2(3):59-62. doi: 10.11648/j.ijsts.20140203.15

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  • @article{10.11648/j.ijsts.20140203.15,
      author = {Hussein Elarabi and N. F. Taha},
      title = {Comparison of Different Methods of Application of Neural Network on Soil Profile of Khartoum State},
      journal = {International Journal of Science, Technology and Society},
      volume = {2},
      number = {3},
      pages = {59-62},
      doi = {10.11648/j.ijsts.20140203.15},
      url = {https://doi.org/10.11648/j.ijsts.20140203.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsts.20140203.15},
      abstract = {Within the last years, four methods have been developed to predict the soil profile and its parameters in Sudan. However, a method making such predictions with the required degree of accuracy and consistency has not yet been developed. In this paper, artificial neural networks, ANNs are used in an attempt to compare between these methods by applying them on large zone contains many sites to select a unified method. A large database of actual measured is used to develop and verify the ANN model. The predicted soil profile found by utilizing ANNs is compared between them. The results indicate that ANNs are a useful technique for predicting the soil profile and its parameters when using anyone of the compared methods.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Comparison of Different Methods of Application of Neural Network on Soil Profile of Khartoum State
    AU  - Hussein Elarabi
    AU  - N. F. Taha
    Y1  - 2014/05/30
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ijsts.20140203.15
    DO  - 10.11648/j.ijsts.20140203.15
    T2  - International Journal of Science, Technology and Society
    JF  - International Journal of Science, Technology and Society
    JO  - International Journal of Science, Technology and Society
    SP  - 59
    EP  - 62
    PB  - Science Publishing Group
    SN  - 2330-7420
    UR  - https://doi.org/10.11648/j.ijsts.20140203.15
    AB  - Within the last years, four methods have been developed to predict the soil profile and its parameters in Sudan. However, a method making such predictions with the required degree of accuracy and consistency has not yet been developed. In this paper, artificial neural networks, ANNs are used in an attempt to compare between these methods by applying them on large zone contains many sites to select a unified method. A large database of actual measured is used to develop and verify the ANN model. The predicted soil profile found by utilizing ANNs is compared between them. The results indicate that ANNs are a useful technique for predicting the soil profile and its parameters when using anyone of the compared methods.
    VL  - 2
    IS  - 3
    ER  - 

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Author Information
  • Head of Geotechnical Department, Building and Road Research Institute, University of Khartoum, Sudan

  • Head of Geotechnical Department, Building and Road Research Institute, University of Khartoum, Sudan

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