.png){kind=icon}
.png){kind=icon}
Return
Efficient algorithm for generalized reliability-based design optimization under fuzzy state assumption constrained by extremely small target generalized failure probability
.png){kind=icon}
PRE
AI
DOI：10.1016/j.probengmech.2026.103934.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
For the commonly encountered scenarios of probabilistic input and fuzzy state in engineering applications, generalized RBDO (GRBDO) aims at an optimal scheme by balancing generalized failure probability (GFP) constraint and performance objective. However, solving GRBDO is time-consuming due to the nested framework of searching optimal design parameters and analyzing GFP constraints, particularly for extremely small target GFP. To address this issue, a sequential offset estimation and deterministic optimization is proposed by combining stratified importance sampling (SODO-IS). In the SODO-IS, the GFP constraint is sequentially replaced as the deterministic one by the performance function offset corresponding to the target GFP, on which the nested framework in GRBDO is decoupled as the sequence of the inverse GFP analysis for estimating offset and the deterministic optimization for searching design parameters. To efficiently estimate the offset corresponding to the extremely small target GFP with rare important region, a stratified IS method, where an explicit and easy-to-sample IS density is adaptively constructed to cover the rare important region by clustering analysis, is developed to reduce the variance of estimating the offset. Due to the sequential decoupling in solving GRBDO assisted by IS variance reduction in estimating offset, the proposed SODO-IS shows higher efficiency than existing methods under acceptable accuracy, which is verified by several examples.
Keywords:
Generalized Reliability-Based Design Optimization
Fuzzy State
Extremely Small Target Generalized Failure Probability
Stratified Importance Sampling
Sequential Offset Estimation
Journal
IF:
3.5
Papers:
1.7K
Citations:
4.1K
Organization
No organization information available