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The use of neural networks for the prediction of cone penetration resistance of silty sands

Abstract

In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.

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References

  1. 1.

    Kim D, Shin Y, Siddiki N (2010) Geotechnical design based on CPT and PMT. Publication FHWA/IN/JTRP-2010/07. Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette. Indiana. doi:10.5703/1288284314264

  2. 2.

    Robertson PK, Cabal KL (2010) Guide to cone penetration testing for geotechnical engineering, 3rd edn. Gregg Drilling and Testing, Signal Hill

  3. 3.

    Park HI, Kim YT (2011) Prediction of strength of reinforced lightweight soil using an artificial neural network. Eng Comput Int J Comput Aid Eng Softw 28(5):600–605

  4. 4.

    Fausett LV (1994) Fundamentals of neural networks: architecture, algorithms, and applications. Prentice-Hall, Englewood Cliffs

  5. 5.

    Najjar YM, Ali HE (1999) Simulating the stress-strain behavior of Nevada sand by ANN. In: Proceedings of the 5th U.S. national congress on computational mechanics (USACM), Boulder, CO

  6. 6.

    Penumadu D, Zhao R (1999) Triaxial compression behavior of sand and gravel using and artificial neural networks (ANN). Comput Geotech 24(3):207–230

  7. 7.

    Erzin Y (2007) Artificial neural networks approach for swell pressure versus soil suction behavior. Can Geotech J 44(10):1215–1223

  8. 8.

    Ozer M, Isik NS, Orhan M (2008) Statistical and neural network assessment of the compression index of clay-bearing soils. Bull Eng Geol Environ 67:537–545

  9. 9.

    Erzin Y, Gumaste SD, Gupta AK, Singh DN (2009) ANN models for determining hydraulic conductivity of compacted fine grained soils. Can Geotech J 46:955–968

  10. 10.

    Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 33:454–459

  11. 11.

    Park HI, Cho CH (2010) Neural network model for predicting the resistance of driven piles. Mar Georesourc Geotech 28(4):324–344

  12. 12.

    Zhao HB (2008) Slope reliability analysis using a support vector machine. Comput Geotech 35:459–467

  13. 13.

    Cho SE (2009) Probabilistic stability analyses of slopes using the ANN-based response surface. Comput Geotech 36:787–797

  14. 14.

    Erzin Y, Cetin T (2012) The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Sci Iran 19(2):188–194

  15. 15.

    Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51:305–313

  16. 16.

    Erzin Y, Cetin T (2014) The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions. Int J Geomech Eng 6(1):1–15

  17. 17.

    Shahin MA, Jaksa MB, Maier HR (2005) Stochastic simulation of settlement prediction of shallow foundations based on a deterministic artificial neural network model. In: Proceedings of the international congress on modelling and simulation, MODSIM 2005, Melbourne, Australia, pp 73–78

  18. 18.

    Erzin Y, Gul T (2013) The use of neural networks for the prediction of the settlement of pad footings on cohesionless soils based on standard penetration test. Int J Geomech Eng 5(6):541–564

  19. 19.

    Erzin Y, Gul T (2014) The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test. Neural Comput Appl 24:891–900

  20. 20.

    Goh ATC (2002) Probabilistic neural network for evaluating seismic liquefaction potential. Can Geotech J 39:219–223

  21. 21.

    Baziar MH, Nilipour N (2003) Evaluation of liquefaction potential using neural-networks and CPT results. Soil Dyn Earthq Eng 23:631–636

  22. 22.

    Hanna AM, Ural D, Saygili G (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn Earthq Eng 27(6):521–540

  23. 23.

    Pathak SR, Dalvi AN (2011) Performance of empirical models for assessment of seismic soil liquefaction. Int J Earth Sci Eng 4:83–86

  24. 24.

    Kumar V, Venkatesh K, Tiwari RP, Kumar Y (2012) Application of ANN to predict liquefaction potential. Int J Comput Eng Sci 2(2):379–389

  25. 25.

    Venkatesh K, Kumar V, Tiwari R (2013) Appraisal of liquefaction potential using neural networks and neuro fuzzy approach. Appl Artif Intel 27(8):700–720

  26. 26.

    Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Environ 74:103–116

  27. 27.

    Shi JJ (2000) Reduction prediction error by transforming input data for neural networks. J Comput Civil Eng 14(2):109–116

  28. 28.

    Yoo C, Kim J-M (2007) Tunneling performance prediction using an integrated GIS and neural network. Comput Geotech 34:19–30

  29. 29.

    Ecemis N, Karaman M (2014) Influence of non-/low plastic fines on cone penetration and liquefaction resistance. Eng Geol 181:48–57

  30. 30.

    Shahin M (2010) Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 47(2):230–243

  31. 31.

    Zurada JM (1992) Introduction to artificial neural systems. West, St. Paul

  32. 32.

    Hecht-Nielsen R (1987) Kolomogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, USA, pp 11–14

  33. 33.

    Maren A, Harston C, Pap R (1990) Handbook of neural computing applications. Academic Press, San Diego

  34. 34.

    Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge

  35. 35.

    Goh ATC (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Geoenviron 120(9):1467–1480

  36. 36.

    Orbanić P, Fajdiga M (2003) A neural network approach to describing the fretting fatigue in aluminum-steel couplings. Int J Fatigue 25:201–207

  37. 37.

    Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2005) Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min 43(2):224–235

  38. 38.

    Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, Dayton, OH, USA, pp 277–280

  39. 39.

    Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236

  40. 40.

    Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

  41. 41.

    Grima MA, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min 36:339–349

  42. 42.

    Haque ME, Sudhakar KV (2002) ANN back-propagation prediction model for fracture toughness in microalloy steel. Int J Fatigue 24:1003–1010

  43. 43.

    Rumelhart DH, Hinton GE, Williams RJ (1986) Chapter 8. In: Rumelhart DE, McClelland JL (eds) Learning internal representation by error propagation: parallel distributed processing, vol 1. MIT Press, Cambridge, MA

  44. 44.

    Najjar YM, Basheer IA, McReynolds R (1996) Neural modelling of Kansas soil swelling. Transp Res 1526:14–19

  45. 45.

    Kim H, Rauch AF, Haas CT (2004) Automated quality assessment of stone aggregates based on laser imaging and a neural network. J Comput Civil Eng 18(1):58–64

  46. 46.

    Singh TN, Gupta AR, Sain R (2006) A comparative analysis of cognitive systems for the prediction of drillability of rocks and wear factor. Geotech Geol Eng 24:299–312

  47. 47.

    Erzin Y, Rao BH, Singh DN (2008) Artificial neural networks for predicting soil thermal resistivity. Int J Therm Sci 47:1347–1358

  48. 48.

    Erzin Y, Rao BH, Patel A, Gumaste SD, Gupta AK, Singh DN (2010) Artificial neural network models for predicting of electrical resistivity of soils from their thermal resistivity. Int J Therm Sci 49:118–130

  49. 49.

    Goh ATC (1995) Back-propagation neural networks for modelling complex systems. Artif Intell Eng 9:143–151

  50. 50.

    Goh ATC (1995) Modelling soil correlations using neural networks. J Comput Civil Eng 9:275–278

  51. 51.

    Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36(1):49–62

  52. 52.

    Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng 18(2):105–114

  53. 53.

    Shahin MA, Jaksa MB (2005) Neural network prediction of pullout capacity of marquee ground anchors. Comput Geotech 32:153–163

  54. 54.

    Demuth H, Beale M, Hagan M (2006) Neural network toolbox user’s guide. The Math Works Inc., Natick

  55. 55.

    Youd TL, Idriss IM, Andrus RD, Arango I, Castro G, Christian JT, Dobry R, Finn WDL, Harder JLF, Hynes ME, Ishihara K, Koester JP, Liao SSC, Marcuson WF, Martin GR, Mitchell JK, Moriwaki Y, Power MS, Robertson PK, Seed RB, Stoke KH (2001) Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J Geotech Geoenviron 127(10):817–833

  56. 56.

    Boulanger RW (2003) High overburden stress effects in liquefaction analyses. J Geotech Geoenviron 129(12):1071–1082

  57. 57.

    Bolton MD, Gui MW (1993) The study of relative density and boundary effects for cone penetration tests in centrifuge. Technical Report: CUED/D-SOILS/TR256. Department of Engineering, Cambridge University

  58. 58.

    Jamiolkowski M, Baldi G, Bellotti R, Ghionna V, Pasqualini E (1985) Penetration resistance and liquefaction of sands. In: Proceedings of the 11th international conference on soil mechanics and foundation engineering, San Francisco

  59. 59.

    Cai GJ, Liu SY, Cheng Y, Zou HF, Du GY, Ren BB, Puppala AJ (2013) In situ evaluation of relative density from piezocone penetration tests of clean sand from China. Geotechnical and geophysical site characterization 4. In: Proceedings of the 4th international conference on site characterization 4, ISC-4, vol 1, pp 207–211

  60. 60.

    Teh CI, Houlsby GT (1991) An analytical study of the cone penetration test in clay. Geotechnique 41(1):17–34

  61. 61.

    Parez L, Fauriel R (1988) Le piezocone ameliorations apportees a la reconnaissance de sols. Rev Francaise de Geotech 44:13–27

  62. 62.

    Darcy H (1856) Les Fontaines Publiques de la Ville de Dijon. Dalmont, Paris

  63. 63.

    Robertson PK (2009) Interpretation of cone penetration tests—a unified approach. Can Geotech J 46(11):1337–1355

  64. 64.

    Robertson PK, Wride CE (1998) Evaluating cyclic liquefaction potential using the cone penetration test. Can Geotech J 35(3):442–459

  65. 65.

    Masters T (1993) Practical neural network recipes in C++. Academic Press, San Diego

  66. 66.

    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

  67. 67.

    Sakellariou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23:419–445

  68. 68.

    Banimahd M, Yasrobi S, Woodward PK (2005) Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification. Comput Geotech 32:377–386

  69. 69.

    Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17:61–72

  70. 70.

    Finol J, Guo YK, Jing XD (2001) A rule based fuzzy model for the prediction of petrophysical rock parameters. J Pet Sci Eng 29:97–113

  71. 71.

    Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. Eng Geol 66(1–2):39–51

  72. 72.

    Kanibir A, Ulusay R, Aydan O (2006) Liquefaction-induced ground deformations on a lake shore (Turkey) and empirical equations for their prediction. IAEG2006, Paper 362

  73. 73.

    Cabalar AF, Cevik A (2009) Modeling damping ratio and shear modulus of sand–mica mixtures using neural networks. Eng Geol 104:31–40

  74. 74.

    Cevik A, Sezer EA, Cabalar AF, Gokceoglu C (2011) Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput 11(2):2587–2594

  75. 75.

    Köroğlu MA, Köken A, Arslan MH, Çevik A (2013) Neural network prediction of the ultimate capacity of shear stud connectors on composite beams with profiled steel sheeting. Sci Iran 20(4):1101–1113

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Acknowledgments

The data from this study were from the European Union Marie Curie Fellowship under Grant No. IRG248218 and TUBITAK Project No. 111M602. The authors wish to thank Research Assistant Mustafa Karaman for his assistance in conducting field tests.

Author information

Correspondence to Yusuf Erzin.

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Cite this article

Erzin, Y., Ecemis, N. The use of neural networks for the prediction of cone penetration resistance of silty sands. Neural Comput & Applic 28, 727–736 (2017). https://doi.org/10.1007/s00521-016-2371-z

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Keywords

  • Artificial neural networks
  • Cone penetration resistance
  • Horizontal coefficient of consolidation
  • Silty sand