Separable Monte Carlo combined with importance sampling for variance reduction
by Anirban Chaudhuri; Raphael T. Haftka
International Journal of Reliability and Safety (IJRS), Vol. 7, No. 3, 2013

Abstract: Monte Carlo (MC) methods are often used to carry out reliability based design of structures. Methods that improve the accuracy of MC simulation include Separable Monte Carlo (Separable MC), Markov Chain Monte Carlo and importance sampling. We explore the utility of combining Separable MC and importance sampling for improving accuracy. The accuracy of the estimates is compared for Standard MC, Separable MC, importance sampling and combined method for a composite plate example and a tuned mass damper example. For these examples Separable MC and importance sampling reduced the error individually by factors of 2-5, and the combination reduced it further by about a factor of 2. The results were also compared with the First Order Reliability Method (FORM). FORM was grossly inaccurate for the tuned mass-damper example which has a failure region bounded by safe regions on either side.

Online publication date: Thu, 10-Oct-2013

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reliability and Safety (IJRS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com