Open Access
Issue
JNWPU
Volume 44, Number 2, April 2026
Page(s) 213 - 221
DOI https://doi.org/10.1051/jnwpu/20264420213
Published online 12 June 2026
  1. LYU Zhenzhou, SONG Shufang, LI Luyi, et al. Fundamental of structure and mechanism reliability design[M]. Xi'an: Northwestern Polytechnical University Press, 2019: 200–206 (in Chinese) [Google Scholar]
  2. HUANG Xiaoxu, CHEN Jianqiao. Reliability analysis based on active learning Kriging model[J]. Chinese Journal of Solid Mechanics, 2016, 37(2): 172–180 (in Chinese) [Google Scholar]
  3. GU XuerongLIU Shuoshi, YANG Siyu. Research on multi-parameter optimization method based on parallel EGO and surrogate-assisted model[J]. CIESC Journal, 2023, 74(2): 1205–1215 (in Chinese). [Google Scholar]
  4. HU H, WANG P, ZHOU H. Sequential reliability analysis for the adjusting mechanism of tail nozzle considering wear degradation[J]. Machines, 2022, 10(2): 613 [Article] [Google Scholar]
  5. WANG E S, WANG Y, XIE B, et al. An adaptive Kriging-based fourth-moment reliability analysis method for engineering structures[J]. Machines, 2024, 14(2): 3247 (in Chinese) [Google Scholar]
  6. YU Mengchen, LONG Xiangyun. Generalized probability and interval hybrid reliability analysis based on two-stage active learning Kriging model[J]. Journal of Mechanical Engineering, 2022, 58(2): 274–288 (in Chinese) [Google Scholar]
  7. FAN Xiaoning, LYU Zhaoguo. Reliability analysis method of crane structure based on dynamic Kriging surrogate model[J]. Chinese Journal of Construction Machinery, 2025, 23(2): 264–269 (in Chinese) [Google Scholar]
  8. CHEN Zhe, YANG Xufeng, CHENG Xin. Active learning method based on improved Kriging model for reliability analysis[J]. Journal of Mechanical Strength, 2021, 43(2): 129–136 (in Chinese) [Google Scholar]
  9. FANG Zhiyong, GUO Xiwei, MA Yatao, et al. Reliability calculation method of improved Kriging model based on distance constraint[J]. Journal of Wuhan University of Technology, 2022, 46(2): 489–494 (in Chinese) [Google Scholar]
  10. HONG Linxiong, LI Huacong, PENG Kai, et al. Structural reliability algorithms of Kriging model based on improved learning strategy[J]. Journal of Northwestern Polytechnical University, 2020, 38(2): 412–419 [Article] (in Chinese) [Google Scholar]
  11. GAO Jin, CUI Haibing, FAN Tao, et al. A structural reliability calculation method based on adaptive Kriging ensemble model[J]. China Mechanical Engineering, 2024, 35(2): 83–92 (in Chinese) [Google Scholar]
  12. ZHI Pengpeng, WANG Zhonglai, LI Yonghua, et al. RMQGS-APS-Kriging-based activate learning structural reliability analysis method[J]. Journal of Mechanical Engineering, 2022, 58(2): 420–429 (in Chinese) [Google Scholar]
  13. CHEN Junyu, FENG Yunwen, TENG Da, et al. Enhanced embedding surrogate modeling method of interactive components reliability evaluation for nose wheel steering mechanism[J]. Reliability Engineering & System Safety, 2025, 262111237 [Google Scholar]
  14. SUN Z, WANG J, LI R, et al. LIF: a new Kriging based learning function and its application to structural reliability analysis[J]. Reliability Engineering and System Safety, 2017, 157: 152–165 [Article] [CrossRef] [Google Scholar]
  15. PENG Changle, CHEN Cheng, GUO Tong, et al. AK-SEUR: an adaptive Kriging-based learning function for structural reliability analysis through sample-based expected uncertainty reduction[J]. Structural Safety, 2024, 106: 102384 [Article] [Google Scholar]
  16. WANG Yanjin, PAN Hao, SHI Yina, et al. A new active-learning estimation method for the failure probability of structural reliability based on Kriging model and simple penalty function[J]. Computer Methods in Applied Mechanics and Engineering, 2023, 410: 116035 [Article] [Google Scholar]
  17. LIU Y, LI L, CHANG Z., et al. A novel optimization-based adaptive Kriging method for structural reliability analysis[J]. Engineering with Computers, 2025, 41(5): 2953–2967 [Article] [Google Scholar]
  18. WANG J, LU Z. An efficient surrogate model method considering the candidate sample pool reduction by safety optimal hypersphere for random-interval mixed reliability analysis[J]. Engineering with Computers, 2024, 40: 795–811 [Article] [Google Scholar]
  19. LAV L Z, GHIFARI A F, PALAR P S. On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning[J]. Reliability Engineering and System Safety, 2021, 215: 107848 [Article] [Google Scholar]
  20. YANG Xufeng, CHENG Xin, LIU Zeqing. Reliability analysis method combining cross-entropy active sampling and ALK model[J]. Journal of Mechanical Engineering, 2024, 60(2): 73–82 (in Chinese) [Google Scholar]
  21. FAN Xin, ZHANG Leigang, YANG Xufeng, et al. A single-loop active learning Kriging method for failure probability upper bound function estimation[J]. Structures, 2025, 79109420 [Article] [Google Scholar]
  22. BORISENKO V, LEORO J, DIDENKO A. Main rotor blade modeling approaches comparison[C]//International Conference on Information Technologies and Mathematical Modeling, 2021. [Google Scholar]
  23. SHAN Bonan, FU Yulong, YE Haijun, et al. Analysis and modeling for typical patrol routes of early warning aircraft[J]. Journal of China Academy of Electronics and Information Technology, 2019, 14(2): 625–633 (in Chinese) [Google Scholar]
  24. 全国滚动轴承标准化技术委员会. 关节轴承额定动载荷与寿命: JB/T 8565—2010[S]. 北京: 机械工业出版社, 2010. [Google Scholar]
  25. ZOU Linjun, WU Yizhong, MAO Huping. Incremental Kriging model rebuilding method and its application in efficient global optimization[J]. Journal of Computer-Aided Design & Computer Graphics, 2011, 23(2): 649–655 (in Chinese) [Google Scholar]
  26. FENG Shizhe, MA Jichao, ZHANG Dequan, et al. Kriging-assisted fatigue life prediction of harmonic reducer flexible wheel under random uncertainty[J]. Chinese Journal of Computational Mechanics, 2023, 40(2): 936–943 (in Chinese) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.