A non-intrusive stochastic finite-element method is
proposed for uncertainty propagation through mechanical systems with
uncertain input described by random variables. A polynomial chaos
expansion (PCE) of the random response is used. Each PCE coefficient
is cast as a multi-dimensional integral when using a projection
scheme. Common simulation schemes, e.g. Monte Carlo Sampling
(MCS) or Latin Hypercube Sampling (LHS), may be used to estimate these
integrals, at a low convergence rate though. As an alternative,
quasi-Monte Carlo (QMC) methods, which make use of quasi-random
sequences, are proposed to provide rapidly converging estimates. The
Sobol' sequence is more specifically used in this paper. The accuracy
of the QMC approach is illustrated by the case study of a truss
structure with random member properties (Young's modulus and cross
section) and random loading. It is shown that QMC outperforms MCS and
LHS techniques for moment, sensitivity and reliability analyses.