Skip to main content

A Spectral Formulation of Stochastic Finite Elements

  • Chapter

Part of the book series: Solid Mechanics and Its Applications ((SMIA,volume 56))

Abstract

Until recently, stochastic structural mechanics has addressed the issue of deterministic structures subjected to random loading. With the availability of more accurate analysis and design tools, however, quantifying the sensitivity of model predictions to uncertainty in the mechanical properties of structures has become possible. It has been observed, with the help of these tools, that this type of uncertainty can be more significant to the overall predictions of a particular structural model than the more traditional fluctuations attributed to external loads. In view of that, recent procedures have been developed for representing uncertainties in the parameters of a structural model, as well as, for propagating this uncertainty to obtain the associated uncertainty in the predicted response. The stochastic finite element method is a procedure for performing such an analysis, whereby the spatial extent of the structure has been represented within the context of the finite element method. This chapter describes a recent implementation of the stochastic finite element method that combines theoretical rigor with generality and efficiency of implementation. Specifically, the Spectral Stochastic Finite Element Method (SSFEM) presented in this chapter addresses the situation where the uncertain material properties are realizations of a spatially fluctuating random field. The formulation relies on discretizing the random processes using spectral expansions, thus eliminating the correlation between the requisite mesh size to meet energy-based convergence criteria, and the scales of fluctuation of the random material properties involved.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Cameron, R.H. and Martin, W.T. (1947) “The orthogonal development of nonlinear functionals in series of Fourier-Hermite functionals”, Ann. Math, Vol. 48, pp. 385–392.

    Article  MathSciNet  MATH  Google Scholar 

  • Chorin, A. J. (1971) “Hermite expansions in Monte-Carlo computation”, Journal of Computational Physics, Vol. 8, pp. 472–482.

    Article  MathSciNet  MATH  Google Scholar 

  • Courant and Hilbert (1953) Methods of Mathematical Physics? Interscience, New York, 1953.

    Google Scholar 

  • Doob, J.L. Stochastic Processes, Wiley, New York.

    Google Scholar 

  • Ghanem, R. and Spanos, P. (1991) Stochastic Finite Elements: A Spectral Approach, Springer-Verlag, New York.

    Book  MATH  Google Scholar 

  • Juncosa, M. (1945) “An integral equation related to the Bessel functions”, Duke Mathematical Journal, Vol. 12, pp. 465–468.

    Article  MathSciNet  MATH  Google Scholar 

  • Kac, M. and Siegert, A.J.F. (1947) “An explicit representation of a stationary gaussian process”, Ann. Math. Stat., Vol. 18, pp.438–442.

    Article  MathSciNet  MATH  Google Scholar 

  • Kakutani, S. (1961) “Spectral analysis of stationary gaussian processes”, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Neyman J. Editor, University of California, Vol. II, pp. 239–247.

    Google Scholar 

  • Karhunen, K. (1960) “Uber lineare methoden in der wahrscheinlichkeitsrechnung”, Amer. Acad. Sci., Fennicade, Ser. A, I, Vol. 37, pp. 3–79, 1947; (Translation: RAND Corporation, Santa Monica, California, Rep. T-131, Aug.).

    Google Scholar 

  • Kolmogorov, A.N. (1950) Foundations of the Theory of Probability, Springer, 1933, (English Translation, Chelsea, New York.)

    Google Scholar 

  • Kree, P. and Soize, C. (1986) Mathematics of Random Phenomena, MIA, Reidel Publishing, Boston, Massachussets.

    Book  MATH  Google Scholar 

  • Loeve, M. (1948) “Fonctions aleatoires du second ordre”, supplement to P. Levy, Processus Stochastic et Mouvement Brownien, Paris, Gauthier Villars.

    Google Scholar 

  • Loeve, M. (1977) Probability Theory, 4th edition, Springer-Verlag, Berlin.

    MATH  Google Scholar 

  • MACSYMA, Reference Manual, Version 12 (1986) Symbolics Inc.

    Google Scholar 

  • Maltz, F.H. and Hitzl, D.L. (1979) “Variance reduction in Monte-Carlo computations using multi-dimensional Hermite polynomials”, Journal of Computational Physics, Vol. 32, pp. 345–376.

    Article  MathSciNet  MATH  Google Scholar 

  • Oden J. T. (1979) Applied Functional Analysis, Prentice-Hall, Englewood Cliffs, New Jersey.

    MATH  Google Scholar 

  • Slepian, D. and Pollak, H.O. (1961) “Prolate spheroidal wave functions; Fourier analysis and uncertainty — I”, Bell System Technical Journal, pp. 43–63.

    Google Scholar 

  • Van Trees, H.L. (1968) Detection, Estimation and Modulation Theory, Part 1, Wiley, New York.

    Google Scholar 

  • Volterra, V. (1913) Lecons sur les Equations Integrales et Integrodifferentielles, Paris: Gauthier Villars.

    Google Scholar 

  • Wiener, N. (1938) “The homogeneous chaos”, Amer. J. Math, Vol. 60, pp. 897–936.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Ghanem, R.G., Spanos, P.D. (1997). A Spectral Formulation of Stochastic Finite Elements. In: Soares, C.G. (eds) Probabilistic Methods for Structural Design. Solid Mechanics and Its Applications, vol 56. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5614-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-5614-1_13

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6366-1

  • Online ISBN: 978-94-011-5614-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics