Abstract
Based on two procedures for efficiently generating conditional samples, i.e. Markov chain Monte Carlo (MCMC) simulation and importance sampling (IS), two reliability sensitivity (RS) algorithms are presented. On the basis of reliability analysis of Subset simulation (Subsim), the RS of the failure probability with respect to the distribution parameter of the basic variable is transformed as a set of RS of conditional failure probabilities with respect to the distribution parameter of the basic variable. By use of the conditional samples generated by MCMC simulation and IS, procedures are established to estimate the RS of the conditional failure probabilities. The formulae of the RS estimator, its variance and its coefficient of variation are derived in detail. The results of the illustrations show high efficiency and high precision of the presented algorithms, and it is suitable for highly nonlinear limit state equation and structural system with single and multiple failure modes. Crown
| Original language | English |
|---|---|
| Pages (from-to) | 658-665 |
| Number of pages | 8 |
| Journal | Reliability Engineering and System Safety |
| Volume | 94 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2009 |
Keywords
- Conditional failure probability
- Importance sampling (IS)
- Markov chain Monte Carlo (MCMC) simulation
- Reliability sensitivity (RS)
- Subset simulation (Subsim)