Keywords: uncertainty propagation, reliability analysis, stochastic modelling, polynomial chaos expansion, linear regression, stochastic collocation method, stochastic Galerkin method, Monte Carlo method.

Regarding the structural reliability many important factors such as the environmental conditions as well as structural properties have to be taken into account in designing of structures. As a result of the growth of powerful computing technology, recently developed procedures in the field of stochastic mechanics have become applicable to realistic engineering systems. This paper focuses on employing a surrogate model based on polynomial chaos expansion in uncertainty quantification for structural reliability analysis. The aim of the paper is to review and compare several approaches such as the stochastic Galerkin method, the stochastic collocation method or linear regression based on Latin hypercube sampling for construction of the polynomial chaos-based approximation of a model response. The advantages and disadvantages of these methods are demonstrated within the comparison with the traditional Monte Carlo method on a simple illustrative example of a frame structure.

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