Classification approach for reliability analysis with stochastic finite-element modeling

Journal of Structural Engineering

Volumn 129, Issue 8, 2003, Pages 1141-1149

  Hurtado, Jorge E ^a   Alvarez, Diego A ^a  

^a UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

Author keywords

Classification; Finite elements; Models; Monte Carlo method; Reliability analysis; Structural reliability

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); EIGENVALUES AND EIGENFUNCTIONS; FINITE ELEMENT METHOD; IMAGE ANALYSIS; MAPPING; MATHEMATICAL MODELS; MECHANICAL PROPERTIES; MONTE CARLO METHODS; PATTERN RECOGNITION; PROBABILITY; RANDOM PROCESSES;

KERNEL METHOD; NONLINEAR MAPPING; STOCHASTIC FINITE-ELEMENTS (SFE); STRUCTURAL RELIABILITY;

STRUCTURAL DESIGN;

EID: 1542263313     PISSN: 07339445     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)0733-9445(2003)129:8(1141)     Document Type: Article

References (32)

1. Anthony, M., and Bartlett, P. L. (1999). Neural network learning: Theoretical foundations, Cambridge University Press, Cambridge, England.
2. Atkinson, K. (1997). The numerical solution of integral equations of the second kind, Cambridge University Press, Cambridge, England.
3. Au, S. K., and Beck, J. L. (1999). "A new adaptive importance sampling scheme." Struct. Safety, 21(2), 135-158.
4. Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press, Oxford.
5. Casciati, F., and Faravelli, L. (1991). Fragility analysis of complex structural systems, Research Studies Press Ltd., Taunton.
6. Cherkassky, V., and Mulier, F. (1998). Learning from data, Wiley, New York.
7. Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based methods, Cambridge University Press, Cambridge, England.
8. Deodatis, G. (1991). "Weighted integral method. I: Stochastic stiffness matrix." J. Eng. Mech., 117(8), 1851-1864.
9. Er, G. K. (1998). "A method for multi-parameter PDF estimation of random variables." Struct. Safety, 20(1), 25-36.
10. Ghanem, R., and Spanos, P. D. (1991). Stochastic finite elements: A spectral approach, Springer-Verlag, New York.
11. Hurtado, J E. (2002). "Analysis of one-dimensional stochastic finite elements using neural networks." Probab. Eng. Mech., 17(1), 35-44.
12. Hurtado, J. E., and Alvarez, D. A. (2001). "Neural network-based reliability analysis - A comparative study." Comput. Methods Appl. Mech. Eng., 191(1-2), 113-132.
13. Kall, P., and Wallace, S. W. (1999). Stochastic programming, Wiley, Chichester.
14. Liu, W. K., Belytschko, T., and Lua, Y J. (1995). "Probabilistic finite element method." Probabilistic structural mechanics handbook, C. Sundararajan, ed., Chapman and Hall, New York, 70-105.
15. Mantoglu, A. (1987). "Digital simulation of multivariate two- and three-dimensional stochastic processes with a spectral turning bands method." Math. Geol., 19(2), 129-149.
16. McLachlan, G., and Peel, D. (2000). Finite mixture models, Wiley, New York.
17. Mignolet, M. P., and Spanos, P. D. (1987). "Recursive simulation of stationary multi-variate random processes - Part I." J. Appl. Mech., 54(9), 674-680.
18. Papadrakakis, M., Papadopoulos, V., and Lagaros, N. D. (1996). "Structural reliability analysis of elastic-plastic structures using neural networks and monte carlo simulation." Comput. Methods Appl. Mech. Eng., 136(2), 145-163.
19. Platt, J. C. (1999). "Fast training of support vector machines using sequential minimal optimization." Advances in kernel methods, B. Schölkopf, C. J. C. Burges, and A. Smola, eds., The MIT Press, Cambridge, Mass., 185-208.
20. Riesz, F., and Sz-Nagy, B. (1990). Functional analysis, Dover, New York.
21. Scott, D. W. (1992). Multivariate density estimation, Wiley, New York.
22. Shinozuka, M. (1987). "Stochastic fields and their digital simulation." Stochastic methods in structural dynamics. G. I Schuëller and M. Shinozuka, eds., Martinus Nijhoff, Dordrecht, The Netherlands, 93-133.
23. Tagliani, A. (1993). "On the application of the maximum entropy principle to the moments problems." J. Math. Phys., 34(1), 326-337.
24. Taha, H. A. (1997). Operations research, 6th Ed., Prentice Hall, Upper Saddle River.
25. Theodoridis, S., and Koutroumbas, K. (1999). Pattern recognition, Academic, London.
26. Vapnik, V. N. (1998). Statistical learning theory, Wiley, New York.
27. Vapnik, V. N. (2000). The nature of statistical learning theory, 2nd Ed., Springer-Verlag, New York.
28. Vidanovic, B. (1999). Statistical modeling by wavelets, Wiley, New York.
29. Wahba, G. (1999). "Support vector machines, reproducing kernel hilbert spaces and randomized GACV." Advances in kernel methods, B. Schölkopf, C. J. C. Burges, and A. Smola, eds., The MIT Press, Cambridge, Mass., 69-88.
30. Yamazaki, F., and Shinozuka, M. (1990). "Simulation of stochastic fields by statistical preconditioning." J. Eng. Mech., 116(2), 268-287.
31. Yamazaki, F., Shinozuka, M., and Dasgupta, G. (1988). "Neumann expansion for stochastic finite-element analysis." J. Eng. Mech., 114(8), 1335-1354.
32. Zeldin, B. A., and Spanos, P. D. (1996). "Random field representation and synthesis using wavelet bases." J. Appl. Mech., 63(12), 946-952.