Structural Engineering and Mechanics

  • 제26권3호
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  • Pages.251-262
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  • 2007
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of artificial neural networks to the response prediction of geometrically nonlinear truss structures

  • Cheng, Jin (Dept.of Bridge Engineering, Tongji University) ;
  • Cai, C.S. (Dept. of Civil and Environmental Engineering, 3418H CEBA, Louisiana State University) ;
  • Xiao, Ru-Cheng (Dept. of Bridge Engineering, Tongji University)
  • 투고 : 2005.10.31
  • 심사 : 2007.01.08
  • 발행 : 2007.06.20
⟨ 이전 논문 다음 논문 ⟩

초록

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

키워드

참고문헌

  1. Adeli, H. (2001), 'Neural networks in civil engineering: 1989-2000', Comput. Aid. Civ. Infrastruct. Eng., 16(2), 126-142 https://doi.org/10.1111/0885-9507.00219
  2. Adhikary, B.B. and Mutsuyoshi, H. (2004), 'Artificial neural networks for the prediction of shear capcity of steel strengthened RC beams', Constr. Build. Mater., 18, 409-417 https://doi.org/10.1016/j.conbuildmat.2004.03.002
  3. Alqedra, M.A and Ashour, A.F. (2005), 'Prediction of shear capacity of single anchors located near a concrete edge using neural networks', Comput. Struct., (in press)
  4. Bathe, K.-J. (1982), Finite Element Procedures in Engineering Analysis, Prentice-Hall, Englewood Cliffs, N.J.
  5. Cheng, Jin, Xiao, Ru-Cheng, and Jiang, Jian-Jing (2004), 'Probabilistic determination of initial cable forces of cable-stayed bridges under dead loads', Int. J Struct. Eng Mech., 17(2)
  6. Cheng, Jin., Cai, C.S., Xiao, Ru-Cheng, and Chen, S.R. (2005), 'Flutter reliability analysis of suspension bridges', J. Wind Eng Industrial Aerodynamics, (in press)
  7. Crisfield, M.A. (1991), Non-linear Finite Element Analysis of Solid and Structures, Wiley, Chichester, U.K.
  8. El-Kassas, E.M.A., Mackie, R.I. and El-sheikh, A.I. (2001), 'Using neural networks in cold-formed steel design', Comput. Struct., 79, 1687-1696 https://doi.org/10.1016/S0045-7949(01)00099-2
  9. Flood, I. and Kartarn, N. (1984), 'Neural networks in civil engineering I: Principles and understandings', J. Comput. Civil Eng, ASCE, 8(2),131-148
  10. Flood, I., Muszynski, L. and Nandy, S. (2001), 'Rapid analysis of externally reinforced concrete beams using neural networks', Comput. Struct., 79, 1553-1559 https://doi.org/10.1016/S0045-7949(01)00033-5
  11. Giunta, A.A., Eldred, M.S. and Castro, J.P. (2004), 'Uncertainty quantification using response surface approximations', 9th ASCE Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability, Albuquerque, New Mexico, July 26-28
  12. Haldar, Achintya and Mahadevan Sankaran (2000), Reliability Assessment Using Stochastic Finite Element Analysis, John Wiley & Sons, New York
  13. Hemez, EM. (2004), 'Uncertainty quantification and the verification and validation of computational models', Damage Prognosis for Aerospace, Civil and Mechanical Systems, Edited by D.J. Inman, C.R Farrar, V. Lopes Jr., and V. Steffen Jr., John Wiley & Sons Ltd., London, United Kingdom, December
  14. Imai, Kiyohiro and Frangopol, Dan M. (2000), 'Response prediction of geometrically nonlinear structures', J. Struct. Eng., ASCE, 126(11),1348-1355 https://doi.org/10.1061/(ASCE)0733-9445(2000)126:11(1348)
  15. Lee, S.C. (2003), 'Prediction of concrete strength using artificial neural networks', Eng Struct., 25, 849-857 https://doi.org/10.1016/S0141-0296(03)00004-X
  16. MacKay, D.J.C. (1992), 'Bayesian interpolation', Neural Comput., 4(3), 415-447 https://doi.org/10.1162/neco.1992.4.3.415
  17. Melchers, Robert E. (1999), Structural Reliability Analysis and Prediction, John Wiley & Sons, New York
  18. Oreta, A.W.C. (2004), 'Simulating size effect on shear strength of RC beams without stirrups using neural networks', Eng. Struct., 26, 681-691 https://doi.org/10.1016/j.engstruct.2004.01.009
  19. Pierce, S.G, Worden, K. and Manson, G. (2006), 'A novel information-gap technique to assess reliability of neural network-based damage detection', J. Sound Vib., 23, 96-111

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