Geomechanics and Engineering

Techno-Press (테크노프레스)
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The use of neural networks for the prediction of swell pressure

  • Erzin, Yusuf (Department of Civil Engineering, Celal Bayar University)
  • Received : 2008.11.24
  • Accepted : 2009.02.04
  • Published : 2009.03.25
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Abstract

Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. The prediction of swell pressures from easily determined soil properties, namely, initial dry density, initial water content, and plasticity index, have been investigated by using artificial neural networks. The results of the constant volume swell tests in oedometers, performed on statically compacted specimens of Bentonite-Kaolinite clay mixtures with varying soil properties, were trained in an ANNs program and the results were compared with the experimental values. It is observed that the experimental results coincided with ANNs results.

Keywords

References

  1. Al-Mhaidip, A. (1999), ''Swelling behavior of expansive shales from the middle region of Saudi Arabia'', Geotech. Geol. Eng., 16, 291-307.
  2. ASTM. (1990). Test methods for one-dimensional swell or settlement potential of cohesive soils, ASTM Method D 4546-4585.
  3. Azam, S. and Abduljauwa, S.N. (2000), "Influence of gypsification on engineering behavior of expansive clay", J. Geotech. Geoenviron. Eng., 126(6), 538-542. https://doi.org/10.1061/(ASCE)1090-0241(2000)126:6(538)
  4. Banimahd, M., Yasrobi, S.S. and Woodward, P.K. (2005), "Artificial neural network for stress-strain behavior of sandy soils: Knowledge based verification", Comput. Geotech., 32, 377-386. https://doi.org/10.1016/j.compgeo.2005.06.002
  5. Barakat, S. and Atom, M.F. (1999), "Comparison between multiple regression analysis and artificial neural Networks in evaluating swelling pressure of clayey soils using three methods", J. Inst. Engineers (India) Civil Eng. Division, 80, 86-93.
  6. Dhowian, A., Erol, O. and Abdulfattah, Y. (1988). Evaluation of expansive soils and foundation methodology in the kingdom of Saudi Arabia, King Abdulaziz City for Science and Technology, Riyad.
  7. El-Sohby, M.A. and El-Sayed, A.R. (1981), "Some factors affecting swelling of clayey soils", Geotech. Eng., 12, 19-39.
  8. El-Sohby, M.A. and El-Sayed, A.R. (1983), "Mineralogy and swelling of expansive clayey soils", Geotech. Eng., 14, 79-87.
  9. Erol, O. and Dhowian, A. (1990), "Swell behavior of arid climate shales from Saudi Arabia", Q. J. Eng. Geol., 23, 243-254. https://doi.org/10.1144/GSL.QJEG.1990.023.03.06
  10. Erzin, Y. (1997). Swell pressure-soil suction relationships. MSc. Thesis, University of Middle East Technical, Ankara.
  11. Erzin, Y. (2007), "Artificial neural networks approach for swell pressure versus soil suction behaviour", Can. Geotech. J., 44, 1215-1223. https://doi.org/10.1139/T07-052
  12. Erzin, Y. and Erol, O. (2004), "Correlations for quick prediction of swell pressures", Electron. J. Geotech. Eng., 9, bundle F.
  13. Erzin, Y. and Erol, O. (2007), "Swell pressure prediction from soil suction measurements", Eng. Geol., 92(3-4), 133-145. https://doi.org/10.1016/j.enggeo.2007.04.002
  14. Erzin, Y., Rao, Hanumantha, B. and Singh, D.H. (2008), "Artificial neural network models for predicting soil thermal resistivity", Int. J. Therm. Sci., 47(10), 1347-1358. https://doi.org/10.1016/j.ijthermalsci.2007.11.001
  15. Finol, J., Guo, Y.K. and Jing, X.D. (2001), "A rule based fuzzy model for the prediction of petrophysical rock parameters", J. Petrol. Sci. Eng., 29, 97-113. https://doi.org/10.1016/S0920-4105(00)00096-6
  16. Goh, A.T.C. (1995), "Modelling soil correlations using neural networks", J. Comput. Civil Eng., ASCE, 9(4), 275- 278. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(275)
  17. Gokceoglu, C. and Zorlu, K. (2004), "A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock", Eng. Appl. Artif. Intel., 17, 61-72. https://doi.org/10.1016/j.engappai.2003.11.006
  18. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neural Networks, 2, 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  19. Kenig, S., Ben-David A., Omer, M. and Sadeh, A. (2001), "Control of properties in injection molding by neural Networks", Eng. Appl. Artif. Intel., 14, 819-823. https://doi.org/10.1016/S0952-1976(02)00006-4
  20. Komornik, A. and David, D. (1969), "Prediction of swelling pressure of clays", J. Soil Mech. Found. Eng. Division, ASCE, 95(SM1), 209-225.
  21. Najjar, Y.M., Basheer, I.A. and Naouss, W.A. (1996), "Neural modelling of Kansas soil swelling", Transport. Res. Record, 1526, 14-19. doi: 10.3141/1526-03.
  22. Orbani , P. and Fajdiga, M. (2003), "A neural network approach to describing the fretting fatigue in aluminumsteel couplings", Int. J. Fatigue, 25, 201-207. https://doi.org/10.1016/S0142-1123(02)00113-5
  23. Penumadu, D. and Zhao, R. (1999), "Triaxial compression behaviour of sand and gravel using artificial neural networks (ANN)", Comput. Geotech., 24, 207-230. https://doi.org/10.1016/S0266-352X(99)00002-6
  24. Perzyk, M. and Kochanski, A.W. (2001), "Prediction of ductile cast iron quality by artificial neural Networks", J. Mater. Process Technol., 109, 305-307. https://doi.org/10.1016/S0924-0136(00)00822-0
  25. Rafiq, M.Y., Bugmann, G. and Easterbrook, D.J. (2001), "Neural network design for engineering applications", Computation. Struct., 79, 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6
  26. Sakellariou, M.G. and Ferentinou, M.D. (2005), "A study of slope stability prediction using neural networks", Geotech. Geol. Eng., 23, 419-445. https://doi.org/10.1007/s10706-004-8680-5
  27. Shahin, M.A. and Jaksa, M.B. (2006), "Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks", Can. Geotech. J., 43, 626-637. https://doi.org/10.1139/t06-029
  28. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2001), "Artificial neural network applications in geotechnical engineering", Aust. Geomech., 36(1), 49-62.
  29. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2002), "Predictive settlement of shallow foundations using neural networks", J. Geotech. Geoenviron. Eng., ASCE, 128(9), 785.
  30. Song, R., Zhang, Q., Tseng, M. and Zang, B. (1995), "The application of artificial neural networks to the investigation of ageing dynamics in 7175 aluminium alloys", Mater. Sci. Eng., C3, 39-41.
  31. Stone, M. (1974), "Cross-validatory choice and assessment of statistical predictions", J. Royal State Society B 36, 111-147.
  32. Teh, C.I., Wong, K.S., Goh, A.T.C. and Jaritngam, S. (1997), "Prediction of pile capacity using neural network". J. Comput. Civil Eng., ASCE, 11(2), 129-138. https://doi.org/10.1061/(ASCE)0887-3801(1997)11:2(129)
  33. Twomey, J.M. and Smith, A.E. (1997). Validation and verification. Artificial neural networks for civil engineers: Fundamentals and applications, N. Kartam, I. Flood, and J.H. Garret, eds. ASCE, New York, 44-64.
  34. Yukselen, Y. and Erzin, Y. (2008), "Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations", Environ. Geol., 54, 1059-1066. https://doi.org/10.1007/s00254-007-0872-x
  35. Zhou, Y. and Wu, X. (1994), "Use of neural networks in the analysis and interpretation of site investigation data", Comput. Geotech., 16, 105-122. https://doi.org/10.1016/0266-352X(94)90017-5

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