DOI QR코드

DOI QR Code

Application of artificial neural networks to a double receding contact problem with a rigid stamp

  • Cakiroglu, Erdogan (Karadeniz Technical University, Civil Engineering Department) ;
  • Comez, Isa (Karadeniz Technical University, Civil Engineering Department) ;
  • Erdol, Ragip (Karadeniz Technical University, Civil Engineering Department)
  • 투고 : 2004.12.07
  • 심사 : 2005.07.06
  • 발행 : 2005.09.30

초록

This paper presents the possibilities of adapting artificial neural networks (ANNs) to predict the dimensionless parameters related to the maximum contact pressures of an elasticity problem. The plane symmetric double receding contact problem for a rigid stamp and two elastic strips having different elastic constants and heights is considered. The external load is applied to the upper elastic strip by means of a rigid stamp and the lower elastic strip is bonded to a rigid support. The problem is solved under the assumptions that the contact between two elastic strips also between the rigid stamp and the upper elastic strip are frictionless, the effect of gravity force is neglected and only compressive normal tractions can be transmitted through the interfaces. A three layered ANN with backpropagation (BP) algorithm is utilized for prediction of the dimensionless parameters related to the maximum contact pressures. Training and testing patterns are formed by using the theory of elasticity with integral transformation technique. ANN predictions and theoretical solutions are compared and seen that ANN predictions are quite close to the theoretical solutions. It is demonstrated that ANNs is a suitable numerical tool and if properly used, can reduce time consumed.

키워드

참고문헌

  1. Arslan, A. and Ince, R. (1996), 'The neural network approximation to the size effect in fracture of cementitious materials', Eng. Fract. Mech., 54(2), 249-261 https://doi.org/10.1016/0013-7944(95)00140-9
  2. Chandrashekhara, K., Okafor, C.A and Jiang, Y.P. (1998), 'Estimation of contact force on composite plates using impact-included strain and neural networks', Composites Part B, 29B, 363-370
  3. Comez, I., Birinci, A. and Erdol, R. (2004), 'Double receding contact problem for a rigid stamp and two elastic layers', European J. Mech. - A/Solids, 23(2), 301-309 https://doi.org/10.1016/j.euromechsol.2003.09.006
  4. Erdogan, F. and Gupta, G. (1972), 'On the numerical solutions of singular integral equations', Quarterly J. Appl. Math., 29, 525-534 https://doi.org/10.1090/qam/408277
  5. Fausett, L. (1994), Fundamentals ofNeural Networks, Prentice-Hall, New Jersey
  6. Hajela, P. and Berke, L. (1991), 'Neurobiological computational models in structural analysis and design', Comput. Struct., 41(4), 657-667 https://doi.org/10.1016/0045-7949(91)90178-O
  7. Hegazy, T., Tully, S. and Marzouk, H. (1998), 'A neural network approach for predicting the structural behavior of concrete slabs', Canadian J. Civil Eng., 25, 668-677 https://doi.org/10.1139/cjce-25-4-668
  8. Ghaboussi, J., Garrett, J.H. and Wu, X. (1991), 'Knowledge based modeling of material behavior with neural networks', J. Eng. Mech., 117(1),132-153 https://doi.org/10.1061/(ASCE)0733-9399(1991)117:1(132)
  9. Guranatram, D.J. and Gem, J.S. (1994), 'Effect of representation on the performance of neural networks in structural engineering applications', Microcomputers in Civil Engineering, 9(2), 97-108 https://doi.org/10.1111/j.1467-8667.1994.tb00365.x
  10. Masri, S.F., Nakamura, M., Chassiakos, A.G. and Caughey, T.K. (1996), 'Neural network approach to detection of changes in structural parameters', J. Eng Mech., 122(4), 350-360 https://doi.org/10.1061/(ASCE)0733-9399(1996)122:4(350)
  11. Ni, H.-G. and Wang, J.-Z. (2000), 'Prediction of compressive strength of concrete by neural networks', Cement and Concrete Research, 30, 1245-1250 https://doi.org/10.1016/S0008-8846(00)00345-8
  12. Ozsahin, T.S., Birinci, A. and Cakiroglu, A.O. (2004), 'Prediction of contact lengths between an elastic layer and two elastic circular punches with neural networks', Struct. Eng Mech., An Int. J., 18(4), 441-459 https://doi.org/10.12989/sem.2004.18.4.441
  13. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), 'Learning internal representations by error propagation', in Rumelhart, D.E. and McClelland, J.L., eds., Parallel Distributed Processing, MIT Press, Cambridge, 318-362
  14. Sanad, A and Saka, M.P. (2001), 'Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks', J. Struct. Eng., 127(7), 818-828 https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(818)
  15. Seibi, A and Al-Alawi, S.M. (1997), 'Prediction of fracture toughness using artificial neural networks (ANNs)', Eng. Fract. Mech., 56(3), 311-319 https://doi.org/10.1016/S0013-7944(96)00076-8
  16. Theocaris, P.S. and Panagiotopoulos, P.D. (1995), 'Generalised hardening plasticity approximated via anisotropicelasticity: A neural network approach', Comput. Meth. Appl. Mech. Eng., 125, 123-139 https://doi.org/10.1016/0045-7825(94)00769-J
  17. Vanluchene, R.D. and Sun, R. (1990), 'Neural networks in structural engineering', Microcomputers in Civil Engineering, 5(3), 207-215 https://doi.org/10.1111/j.1467-8667.1990.tb00377.x
  18. Xiang, Y. and Tso, S.K. (2002), 'Detection and classification of flaws in concrete structure using bispectra and neural networks', NDT&E International, 35, 19-27 https://doi.org/10.1016/S0963-8695(01)00018-4
  19. Zurada, J.M. (1992), Introduction to Artificial Neural Systems, West Publishing Company, New York

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