Reliability–sensitivity analysis using dimension reduction methods and saddlepoint approximations
Article first published online: 18 SEP 2012
Copyright © 2012 John Wiley & Sons, Ltd.
International Journal for Numerical Methods in Engineering
Volume 93, Issue 8, pages 857–886, 24 February 2013
How to Cite
Huang, X. and Zhang, Y. (2013), Reliability–sensitivity analysis using dimension reduction methods and saddlepoint approximations. Int. J. Numer. Meth. Engng., 93: 857–886. doi: 10.1002/nme.4412
- Issue published online: 29 JAN 2013
- Article first published online: 18 SEP 2012
- Manuscript Accepted: 8 AUG 2012
- Manuscript Revised: 7 JUL 2012
- Manuscript Received: 16 AUG 2010
- dimension reduction method;
- failure probability;
- saddlepoint approximation
Reliability–sensitivity, which is considered as an essential component in engineering design under uncertainty, is often of critical importance toward understanding the physical systems underlying failure and modifying the design to mitigate and manage risk. This paper presents a new computational tool for predicting reliability (failure probability) and reliability–sensitivity of mechanical or structural systems subject to random uncertainties in loads, material properties, and geometry. The dimension reduction method is applied to compute response moments and their sensitivities with respect to the distribution parameters (e.g., shape and scale parameters, mean, and standard deviation) of basic random variables. Saddlepoint approximations with truncated cumulant generating functions are employed to estimate failure probability, probability density functions, and cumulative distribution functions. The rigorous analytic derivation of the parameter sensitivities of the failure probability with respect to the distribution parameters of basic random variables is derived. Results of six numerical examples involving hypothetical mathematical functions and solid mechanics problems indicate that the proposed approach provides accurate, convergent, and computationally efficient estimates of the failure probability and reliability–sensitivity. Copyright © 2012 John Wiley & Sons, Ltd.