A wavelet-based model for stochastic analysis of beam structures is pr
esented. In this model, the random processes representing the stochast
ic material and geometric properties are treated as stationary Gaussia
n processes with specified mean and correlation functions. Using the K
arhunen-Loeve expansion, the process is represented as a linear sum of
orthonormal eigenfunctions with uncorrelated random coefficients. The
correlation and the eigenfunctions are approximated as truncated line
ar sums of compactly supported orthogonal wavelets, and the integral e
igenvalue problem is converted to a finite dimensional eigenvalue prob
lem. The energy-principle-based finite element approach is used to obt
ain the equilibrium and boundary conditions, Neumann expansion of the
stiffness matrix is used to write the nodal displacement vector in ter
ms of random coefficients. The expectation operator is applied to the
nodal displacements and their squares to obtain the mean and standard
deviation of the displacements. Studies show that the results obtained
using this method compare well with Monte Carlo and semianalytical te
chniques.