GALERKIN SAMPLING METHOD FOR STOCHASTIC MECHANICS PROBLEMS

A numerical method for solving stochastic mechanics problems by repres enting the solution using a small number of random parameters is prese nted. In essence, the method is a Galerkin approximation in the sample space. The associated projection of the solution into the space of si mple random variables reduces the stochastic problem to a set of deter ministic problems. Alternatively, this method can be viewed as a modif ied-for computational efficiency-stratified sampling method. Several e xamples are considered involving the use of the Loeve-Karhunen expansi on for a stochastic field approximation. The examples deal with the de termination of the natural frequencies and of the seismic response of a beam with random rigidity.