Galerkin Sampling Method for Stochastic Mechanics Problems
Publication: Journal of Engineering Mechanics
Volume 120, Issue 5
Abstract
A numerical method for solving stochastic mechanics problems by representing the solution using a small number of random parameters is presented. In essence, the method is a Galerkin approximation in the sample space. The associated projection of the solution into the space of simple random variables reduces the stochastic problem to a set of deterministic problems. Alternatively, this method can be viewed as a modified—for computational efficiency—stratified sampling method. Several examples are considered involving the use of the Loeve‐Karhunen expansion for a stochastic field approximation. The examples deal with the determination of the natural frequencies and of the seismic response of a beam with random rigidity.
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Published In
Journal of Engineering Mechanics
Volume 120 • Issue 5 • May 1994
Pages: 1091 - 1106
Copyright
Copyright © 1994 American Society of Civil Engineers.
History
Received: May 28, 1993
Published online: May 1, 1994
Published in print: May 1994
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