Abstract
To improve the efficiency and accuracy of rare-event reliability analysis of complex structures, an advanced adaptive kriging-based candidate sample reduction (AK-CSR) method is proposed by integrating the CSR strategy and the advanced AK method. Through the improved first-order reliability method and the updated kriging model (KM), the accurate most probable failure point can be obtained with KM updating. By domain constraint and distance constraint functions, the CSR strategy can ceaselessly find desired samples to update the KM. The proposed method was verified using three numerical examples and two engineering examples. The results demonstrated that the AK-CSR method can be used to perform rare-event reliability analysis of complex structures and improve computational efficiency while maintaining good accuracy. Moreover, this study offers a useful insight into reliability-based design optimisation of complex structures and enriches the field of structural reliability theory. © 2025 ICE Publishing. All rights reserved.
| Original language | English |
|---|---|
| Journal | Proceedings of the Institution of Civil Engineers: Transport |
| DOIs | |
| Publication status | Online published - 30 Mar 2025 |
Funding
This work was supported by the National Natural Science Foundation of China (grant nos 52275471 and 52105136) and the Hong Kong Scholar Programme (grant no. XJ2022013). The authors would like to thank these organisations.
Research Keywords
- active learning
- importance sampling
- kriging model
- mathematical modelling
- numerical modelling
- rare-event reliability analysis
- sample reduction
- uncertainty, reliability & risk
Fingerprint
Dive into the research topics of 'Adaptive surrogate-assisted sampling pool reduction strategy for low failure probability estimation'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver