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Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks

Received: 22 May 2014    Accepted: 09 June 2014    Published: 20 June 2014
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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.

DOI 10.11648/j.sr.20140203.13
Published in Science Research (Volume 2, Issue 3, June 2014)
Page(s) 43-48
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), 2024. Published by Science Publishing Group

Keywords

Artificial Neural Networks, Soil Profile, Soil Parameters, Khartoum, Sudan

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

  • Building and Road Research Institute, University of Khartoum, Sudan

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  • APA Style

    Hussein Elarabi, Nahed F. Taha. (2014). Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks. Science Research, 2(3), 43-48. https://doi.org/10.11648/j.sr.20140203.13

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    ACS Style

    Hussein Elarabi; Nahed F. Taha. Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks. Sci. Res. 2014, 2(3), 43-48. doi: 10.11648/j.sr.20140203.13

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    AMA Style

    Hussein Elarabi, Nahed F. Taha. Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks. Sci Res. 2014;2(3):43-48. doi: 10.11648/j.sr.20140203.13

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  • @article{10.11648/j.sr.20140203.13,
      author = {Hussein Elarabi and Nahed F. Taha},
      title = {Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks},
      journal = {Science Research},
      volume = {2},
      number = {3},
      pages = {43-48},
      doi = {10.11648/j.sr.20140203.13},
      url = {https://doi.org/10.11648/j.sr.20140203.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sr.20140203.13},
      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.},
     year = {2014}
    }
    

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    T1  - Developing of Prediction Models for Soil Profile and Its Parameters Using Artificial Neural Networks
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    AU  - Nahed F. Taha
    Y1  - 2014/06/20
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    AB  - 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.
    VL  - 2
    IS  - 3
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