Browse > Article
http://dx.doi.org/10.12989/sem.2007.26.3.251

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)
Publication Information
Structural Engineering and Mechanics / v.26, no.3, 2007 , pp. 251-262 More about this Journal
Abstract
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.
Keywords
artificial neural networks; geometrically nonlinear analysis; truss structures; uncertainties; response;
Citations & Related Records

Times Cited By Web Of Science : 2  (Related Records In Web of Science)
Times Cited By SCOPUS : 2
연도 인용수 순위
1 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)
2 Flood, I., Muszynski, L. and Nandy, S. (2001), 'Rapid analysis of externally reinforced concrete beams using neural networks', Comput. Struct., 79, 1553-1559   DOI   ScienceOn
3 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
4 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   DOI   ScienceOn
5 Adeli, H. (2001), 'Neural networks in civil engineering: 1989-2000', Comput. Aid. Civ. Infrastruct. Eng., 16(2), 126-142   DOI   ScienceOn
6 Flood, I. and Kartarn, N. (1984), 'Neural networks in civil engineering I: Principles and understandings', J. Comput. Civil Eng, ASCE, 8(2),131-148
7 Haldar, Achintya and Mahadevan Sankaran (2000), Reliability Assessment Using Stochastic Finite Element Analysis, John Wiley & Sons, New York
8 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
9 Imai, Kiyohiro and Frangopol, Dan M. (2000), 'Response prediction of geometrically nonlinear structures', J. Struct. Eng., ASCE, 126(11),1348-1355   DOI   ScienceOn
10 Lee, S.C. (2003), 'Prediction of concrete strength using artificial neural networks', Eng Struct., 25, 849-857   DOI   ScienceOn
11 Crisfield, M.A. (1991), Non-linear Finite Element Analysis of Solid and Structures, Wiley, Chichester, U.K.
12 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   DOI   ScienceOn
13 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)
14 Bathe, K.-J. (1982), Finite Element Procedures in Engineering Analysis, Prentice-Hall, Englewood Cliffs, N.J.
15 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)
16 MacKay, D.J.C. (1992), 'Bayesian interpolation', Neural Comput., 4(3), 415-447   DOI
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   DOI   ScienceOn
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