Details
| Original language | English |
|---|---|
| Pages (from-to) | 1218-1227 |
| Number of pages | 10 |
| Journal | Chinese journal of aeronautics |
| Volume | 33 |
| Issue number | 4 |
| Early online date | 17 Mar 2020 |
| Publication status | Published - Apr 2020 |
Abstract
The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10−5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.
Keywords
- Active learning Kriging model, Adaptive linked importance sampling, Reliability analysis, Sensitivity analysis, Small failure probability
ASJC Scopus subject areas
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Chinese journal of aeronautics, Vol. 33, No. 4, 04.2020, p. 1218-1227.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method
AU - Liu, Fuchao
AU - Wei, Pengfei
AU - Zhou, Changcong
AU - Yue, Zhufeng
N1 - Funding information: This work is supported by National Natural Science Foundation of China (Nos. 51905430 , 51608446 ). Also, the Fundamental Research Fund for Central Universities (No. 3102018zy011 ) is gratefully acknowledged. The second author would also like to acknowledge the supports of Alexander von Humboldt Foundation of Germany and the Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University.
PY - 2020/4
Y1 - 2020/4
N2 - The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10−5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.
AB - The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10−5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.
KW - Active learning Kriging model
KW - Adaptive linked importance sampling
KW - Reliability analysis
KW - Sensitivity analysis
KW - Small failure probability
UR - http://www.scopus.com/inward/record.url?scp=85083308393&partnerID=8YFLogxK
U2 - 10.1016/j.cja.2019.12.032
DO - 10.1016/j.cja.2019.12.032
M3 - Article
AN - SCOPUS:85083308393
VL - 33
SP - 1218
EP - 1227
JO - Chinese journal of aeronautics
JF - Chinese journal of aeronautics
SN - 1000-9361
IS - 4
ER -