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Using artificial neural network to predict dry density of soil from thermal conductivity Cover

Using artificial neural network to predict dry density of soil from thermal conductivity

Materials and Geoenvironment
Volume 64 (2017): Issue 3 (September 2017)
By: Oluseun Adetola Sanuade,  Rasheed Babatunde Adesina,  Joel Olayide Amosun,  Akindeji Opeyemi Fajana and  Olayiwola Grace Olaseeni  
Open Access
|Dec 2017

References

  1. [1] Oladunjoye, M.A., Sanuade, O.A. (2012b): Thermal diffusivity, thermal effusivity and specific heat of soil in Olorunsogo power plant, Southwestern Nigeria. IJRR in Appl Sci, 13(2), pp. 502-521.10.5402/2012/591450
    Search in Google ScholarBack to article
  2. [2] Zhao, J. (1992): Geohydrological and thermal aspects of deep underground waste disposal. In: Proceedings of the Second International Conference on Environmental Issues and Waste Management in Energy and Minerals Production. Balkema: Rotterdam; pp. 669-676.
    Search in Google ScholarBack to article
  3. [3] Rao, M.V.B.B.G., Singh, D.N. (1999): A generalized relationship to estimate thermal resistivity of soils. Canadian Geotechnical Journal., 36(4), pp. 767-773.10.1139/t99-037
    Open DOISearch in Google ScholarBack to article
  4. [4] Noborio, K., Mcinnes, K.J., Heilman, J.L. (1996): Two-dimensional model for water, heat, and solute transport in furrow-irrigated soil: I. Theory, II Field evaluation. Soil Science Society of America Journal, 60, pp. 1001-1021.10.2136/sssaj1996.03615995006000040007x
    Open DOISearch in Google ScholarBack to article
  5. [5] Salomone, L.A., Kovacs, W.D., Kusuda, T. (1984): Thermal performance of fine-grained soils. Journal of Geotechmical and Geoenvironmental Engineering, 110(3), pp. 359-374.10.1061/(ASCE)0733-9410(1984)110:3(359)
    Search in Google ScholarBack to article
  6. [6] Mitchell, J.K. (1991): Conduction phenomena: from theory to geotechnical practice. Geotechnique, 41(3), pp. 299-340. 10.1680/geot.1991.41.3.299
    Open DOISearch in Google ScholarBack to article
  7. [7] Naidu, A.D., Singh, D.N. (2004): A generalized procedure for determining thermal resistivity of soils. International Journal of Thermal Sciences, 43, pp. 43-51.10.1016/S1290-0729(03)00103-0
    Open DOISearch in Google ScholarBack to article
  8. [8] Zhang, J.R., Liu, Z.Q. (2006): A study on the convective heat transfer coefficient of concrete in wind tunnel experiment. China Civil Engineering Journal, 39(9), pp. 39-42.
    Search in Google ScholarBack to article
  9. [9] Tarnawski, V.R., Leong, W.H. (2000): Thermal conductivity of soils at very low moisture content and moderate temperatures. Transport in Porous Media, 41(2), pp. 137-147.10.1023/A:1006738727206
    Open DOISearch in Google ScholarBack to article
  10. [10] Oladunjoye, M.A., Sanuade, O.A. (2012a): In-situ determination of thermal resistivity of soil: case study of Olorunsogo Power plant, Southwestern Nigeria. ISRN Civil Engineering Volume 2012, doi:10.5402/2012/591450.
    Open DOISearch in Google ScholarBack to article
  11. [11] Akintola, J.O. (1986): Rainfall distribution in Nigeria: 1892-1983, Impact Publishers (Nig.) Ltd, Ibadan.
    Search in Google ScholarBack to article
  12. [12] Jones, H.A., Hockey, R.D. (1964): The geology of part of South-Western Nigeria. Geological Survey Nigeria Bulletin, 40, pp. 725-731.
    Search in Google ScholarBack to article
  13. [13] Krishanaiah, S. (2003): Centrifuge modelling of heat migration in geomaterials. Ph.D. Thesis, IIT Bombay: India.
    Search in Google ScholarBack to article
  14. [14] Sheldrick, B.H., Wang, C. (1993): Particle size distribution. P. 499-511. In Carter (ed.) Soil sampling and methods analysis. Canadian Society of Soil Science: Lewis Publishers. Ann Arbor.
    Search in Google ScholarBack to article
  15. [15] ASTM D7928-17 (2017): Standard test method for particle size distribution (gradation) of fine grained soils using the sedimentation (hydrometer) analysis. ASTM International, West Conshohocken, PA.
    Search in Google ScholarBack to article
  16. [16] ASTM D7380-15 (2015): Standard test method for soil compaction determination at shallow depths using 5-lb (2.3 kg) dynamic cone penetrometer, ASTM International, West Conshohocken, PA.
    Search in Google ScholarBack to article
  17. [17] Haykin, S. S. (2009): Neural networks and learning machines. Pearson Education Upper Saddle River, vol. 3.
    Search in Google ScholarBack to article
  18. [18] Chantasut, N., Charoenjit, C., Tanprasert, C. (2004): Predictive mining of rainfall predictions using artificial neural networks for Chao Phraya River. In: Proceedings of the 4th International Conference of the Asian Federation of Information Technology in Agriculture and the 2nd World Congress on Computers in Agriculture and Natural Resources, Thailand: Bangkok; pp. 9-12.
    Search in Google ScholarBack to article
  19. [19] Atkinson, P.M., Tatnall, A. (1997): Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18(4), pp. 699-709.10.1080/014311697218700
    Open DOISearch in Google ScholarBack to article
  20. [20] Gokceoglu, C., Zorlu, K. (2004): A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence 17(1), pp. 61-72.10.1016/j.engappai.2003.11.006
    Open DOISearch in Google ScholarBack to article
  21. [21] Torabi-Kaveh, M., Naseri, F., Saneie, S., Sarshari, B. (2014): Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arabian Journal of Geosciences, 9, pp. 1-9.10.1007/s12517-014-1331-0
    Search in Google ScholarBack to article
DOI: https://doi.org/10.1515/rmzmag-2017-0012 | Journal eISSN: 1854-7400
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Language: English
Page range: 169 - 180
Submitted on: Apr 20, 2017
|
Accepted on: May 25, 2017
|
Published on: Dec 29, 2017
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
thermal conductivity,
ANN,
MATLAB,
prediction,
model
Related subjects:
Computer sciences,
Databases and data mining,
Geosciences,
Geodesy,
Geology and mineralogy,
Materials sciences,
Materials characterization and properties

© 2017 Oluseun Adetola Sanuade, Rasheed Babatunde Adesina, Joel Olayide Amosun, Akindeji Opeyemi Fajana, Olayiwola Grace Olaseeni, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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