Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity

International Journal of Thermal Sciences

Volumn 49, Issue 1, 2010, Pages 118-130

  Erzin, Yusuf ^a   Rao, B Hanumantha ^b   Patel, A ^b   Gumaste, S D ^b   Singh, D N ^b  

^a CELAL BAYAR UNIVERSITY   (Turkey)

^b INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Artificial neural networks; Electrical resistivity; Generalized relationships; Soils; Thermal resistivity

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; COMPACTION DENSITIES; DEGREE OF SATURATIONS; ELECTRICAL RESISTIVITY; ENGINEERING PROJECT; GROUND MODIFICATION; HIGH VOLTAGE; MOISTURE CONTENTS; NUCLEAR WASTE DISPOSAL; POWER CABLES; SOIL TYPES; THERMAL RESISTIVITY;

BACKPROPAGATION; COMPACTION; ELECTRIC CONDUCTIVITY; ENERGY CONVERSION; FLUIDIZATION; FLUIDIZED BEDS; GEOLOGIC MODELS; KNOWLEDGE ENGINEERING; PROFESSIONAL ASPECTS; RADIOACTIVE WASTE DISPOSAL; RADIOACTIVE WASTES; SOILS; THERMAL CONDUCTIVITY; WASTE DISPOSAL;

NEURAL NETWORKS;

EID: 71849087174     PISSN: 12900729     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijthermalsci.2009.06.008     Document Type: Article

References (64)

1. Kalinski R.J., and Kelly W.E. Estimating water content of soils from electrical resistivity. Geotechnical Testing Journal 16 3 (1993) 323-329
2. McCarter W. The electrical resistivity characteristics of compacted clays. Geotechnique 34 2 (1984) 263-267
3. O. Mazac, M. Cislerove, W.E. Kelly, I. Landa, D. Venhodova, Determination of hydraulic conductivities by surface geoelectrical methods, in: S. Ward, (Ed.), Geotechnical and Environmental Geophysics, Vol II, Soc. Explor. Geophys. (1990) 125-131.
4. Ronald A.E., and Ronald C.G. Electrical resistivity used to measure liquefaction of sand. Journal of Geotechnical Engineering 108 GT5 (1982) 779-783
5. D.W. Schultz, B.M. Duff, W.R. Peters, Performance of an Electrical Resistivity Technique for Detecting and Locating Geomembrane Failures, International Conference on Geomembranes, Denver, USA, (1984) 445-449.
6. McCollum B., and Logan K.H. Electrolytic corrosion of Iron in soils. Bureau of Standards, Technologic Paper 24 (1930)
7. Tagg G.F. Earth Resistances (1964), Newnes, London
8. Butterfield R., and Jhonston I.W. The influence of electro-osmosis on metallic piles in clay. Geotechnique 30 1 (1980) 17-38
9. Gunnink B.W., and El-Jayyousi J. Soil-fabric measurement using conduction phase porosimetry. Journal of Geotechnical Engineering Division 119 6 (1993) 1019-1035
10. Shea P.F., and Luthin J.N. An investigation of the use of the four electrode probe for measuring soil salinity in situ. Soil Science 92 (1961) 331-339
11. Z.S. Abu-Hassanein, Use of electrical resistivity measurement as a quality control tool for compacted clay liners, M.S. Thesis, University of Wisconsin, Madison (1994).
12. Thomas H.R. Modeling two-dimensional heat and moisture transfer in soils including gravity effects. International Journal of Numerical and Analytical Methods in Geomechanics 9 (1985) 573-578
13. Singh D.N., and Rao M.V.B.B.G. Laboratory measurement of soil thermal resistivity. Geotechnical Engineering Bulletin 7 3 (1998) 179-199
14. Rao M.V.B.B.G., and Singh D.N. A generalized relationship to estimate thermal resistivity of soils. Canadian Geotechnical Journal 36 4 (1999) 767-773
15. Singh D.N., and Devid K. Generalized relationships for estimating soil thermal resistivity. Experimental. Thermal and Fluid Science 22 (2000) 133-143
16. Kolay P.K., and Singh D.N. Application of coal ash in fluid thermal beds. Journal of Materials Civil Engineering 14 5 (2002) 441-444
17. Singh D.N., Kuriyan S.J., and Manthena K.C. A generalized relationships between soil electrical and thermal resistivities. Experimental Thermal and Fluid Science 25 (2001) 175-181
18. Sreedeep S., Reshma A.C., and Singh D.N. Generalized relationship for determining soil electrical resistivity from its thermal resistivity. Experimental Thermal and Fluid Science 29 (2005) 217-226
19. Toll D.G. Artificial intelligence applications in geotechnical engineering. Electronic Journal of Geotechnical Engineering 1 (1996)
20. Shahin M.A., Jaksa M.B., and Maier H.R. Artificial neural network applications in geotechnical engineering. Australian Geomechanics 36 1 (2001) 49-62
21. Banimahd M., Yasrobi S.S., and Woodward P. Artificial neural network for stress-strain behavior of sandy soils: knowledge based verification. Computers and Geotechnics 32 (2005) 377-386
22. Caglar N., and Arman H. The applicability of neural networks in the determination of soil properties. Bulletin of Engineering Geology and the Environment 66 (2007) 295-301
23. Kim Y.S., and Kim B.T. Use of artificial neural networks in the prediction of liquefaction resistance of sands. Journal of Geotechnical and Geoenvironmental Engineering 132 11 (2006) 1502-1504
24. Kurup P.U., and Griffin E.P. Prediction of soil composition from CPT data using general regression neural network. Journal of Computing in Civil Engineering 20 4 (2006) 281-289
25. Sinha S.K., and Wang M.C. Artificial neural network prediction models for soil compaction and permeability. Geotechnical and Geological Engineering (August 17, 2007)
26. Lee I.M., and Lee J.H. Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics 18 3 (1996) 189-200
27. Teh C.I., Wong K.S., Goh A.T.C., and Jaritngam S. Prediction of pile capacity using neural networks. Journal of Computing in Civil Engineering 11 2 (1997) 129-138
28. Shahin M.A., Maier H.R., and Jaksa M.B. Predicting settlement of shallow foundations using neural networks. Journal of Geotechnical and Geoenvironmental Engineering 128 9 (2002) 785-793
29. Erzin Y., Rao B.H., and Singh D.N. Artificial neural networks for predicting soil thermal resistivity. International Journal of Thermal Science 47 10 (2008) 1347-1358
30. Ellis G.W., Yao C., Zhao R., and Penumadu D. Stress-strain modeling of sands using artificial neural networks. Journal of Geotechnical Engineering 121 5 (1995) 429-435
31. Gandhidasan P., and Mohandes M.A. Predictions of vapor pressures of aqueous desiccants for cooling applications by using artificial neural networks. Applied Thermal Engineering 28 (2008) 126-135
32. Hecht-Nielsen R. Neurocomputing (1990), Addison-Wesley, Boston
33. Zurada J.M. Introduction to Artificial Neural Systems (1992), West, St. Paul, Minn
34. Fausett L.V. Fundamentals of Neural Networks: Architecture, Algorithms and Applications (1994), Prentice-Hall, Englewood Cliffs, N.J
35. Ripley B.D. Pattern Recognition and Neural Networks (1996), Cambridge University Press, Cambridge
36. Orbanić P., and Fajdiga M. A neural network approach to describing the fretting fatigue in aluminum-steel couplings. International Journal of Fatigue 25 (2003) 201-207
37. Sonmez H., Gokceoglu C., Nefeslioglu H.A., and Kayabasi A. Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanical and Mining Sciences 43 2 (2005) 224-235
38. Kaastra I., and Boyd M. Designing a neural network for forecasting financial and economic time series. Neurocomputing 10 3 (1996) 215-236
39. Kanellopoulas, and Wilkinson G.G. Strategies and best practice for neural network image classification. International Journal of Remote Sensing 18 (1997) 711-725
40. Grima M.A., and Babuska R. Fuzzy model for the prediction of unconfined compressive strength of rock samples. International Journal of Rock Mechanical and Mining Science 36 (1999) 339-349
41. Haque M.E., and Sudhakar K.V. ANN back-propagation prediction model for fracture toughness in microalloy steel. International Journal of Fatigue 24 (2002) 1003-1010
42. Najjar Y.M., Basheer I.A., and Naouss W.A. Neural modeling of Kansas soil swelling. Transportation Research Record 1526 (1996) 14-19
43. Kim H., Rauch A.F., and Haas C.T. Automated quality assessment of stone aggregates based on laser imaging and a neural network. Journal of Computing in Civil Engineering 18 1 (2004) 58-64
44. Goh A.T.C. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering 9 (1995) 143-151
45. Goh A.T.C. Modeling soil correlations using neural networks. Journal of Computing in Civil Engineering 9 4 (1995) 275-278
46. Amegashie F., Shang J.Q., Yanful E., Ding W., and Al-Martini S. Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil. Canadian Geotechnical Journal 43 (2006) 100-109
47. Shi J.J. Reduction prediction error by transforming input data for neural networks. Journal of Computing in Civil Engineering 18 2 (2000) 105-114
48. Shahin M.A., Maier H.R., and Jaksa M.B. Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering 18 2 (2004) 105-114
49. Shahin M.A., and Jaksa M.B. Neural network prediction of pullout capacity of marquee ground anchors. Computers and Geotechnics 32 (2005) 153-163
50. Demuth H., Beale M., and Hagan M. Neural Network Toolbox User's Guide (2006), The Math Works, Inc., Natick, Mass
51. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 (1993) 525-533
52. ASTM. D-422-63 Standard Test Method for Particle Size Analysis of Soils. Annual Book of ASTM Standards. 04.08 (1994), ASTM International, West Conshohocken, USA pp. 10-16
53. Astm D. 5550, Standard test method for specific gravity of soil solids by gas pycnometer, Annual book of ASTM standards. 04.08 (2001), ASTM, West Conshohocken, PA, USA 1-4
54. Dalinaidu A., and Singh D.N. A generalized procedure for determining thermal resistivity of soils. International Journal of Thermal Sciences 43 1 (2004) 43-51
55. Twomey J.M., and Smith A.E. Validation and verification. In: Kartam N., Flood I., and Garrett J.H. (Eds). Artificial Neural Networks for Civil Engineers: Fundamentals and Applications (1997), ASCE, New York 44-64
56. Stone M. Cross-validatory choice and assessment of statistical predictions. Journal of Royal Statistical Society B 36 (1974) 111-147
57. Hornik K., Stinchcombe M., and White H. Multilayer feed forward networks are universal approximators. Neural Networks 2 (1989) 359-366
58. Nawari N.O., Liang R., and Nusariat J. Artificial intelligence techniques for the design and analysis of deep foundations. Electronic Journal of Geotechnical Engineering (1999). http://geotech.civeng.okstate.edu/ejge/ppr9909/index.html
59. Caudill M. Neural Networks Primer, Part III. Al Expert 3 6 (1988) 59-61
60. Sakellariou M.G., and Ferentinou M.D. A study of slope stability prediction using neural networks. Geotechnical and Geological Engineering 23 (2005) 419-445
61. Finol J., Guo Y.K., and Jing X.D. A rule based fuzzy model for the prediction of petrophysical rock parameters. Journal of Petroleum Science and Engineering 29 (2001) 97-113
62. Gokceoglu C. A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. Engineering Geology 66 (2002) 39-51
63. Erzin Y. Artificial neural networks approach for swell pressure versus soil suction behavior. Canadian Geotechnical Journal 44 (2007) 1215-1223
64. Yukselen Y., and Erzin Y. Artificial neural networks approach for zeta potential of montmorillonite in the presence of different cations. Environmental Geology 54 (2008) 1059-1066