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Reliability Analysis Method of Complex Structures Based on Active Learning PC-Kriging Model
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Jiqing Chen1, 2, Yuqi Zhang1, 2, Fengchong Lan1, 2, Yunjiao Zhou1, 2, Junfeng Wang1, 2
Automotive Engineering | 2025, 47(2) : 383 - 390
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Automotive Engineering | 2025, 47(2): 383-390
Reliability Analysis Method of Complex Structures Based on Active Learning PC-Kriging Model
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Jiqing Chen1, 2, Yuqi Zhang1, 2, Fengchong Lan1, 2, Yunjiao Zhou1, 2, Junfeng Wang1, 2
Affiliations
  • 1 School of Mechanical & Automotive Engineering，South China University of Technology，Guangzhou 510640
  • 2 Guangdong Province Key Laboratory of Automotive，Guangzhou 510640
Published: 2025-02-25 doi: 10.19562/j.chinasae.qcgc.2025.02.019
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Constructing accurate surrogate models is an effective solution to addressing the problem of multi-dimensional design variables and implicit nonlinear responses in the reliability design of complex structures. However, using experiment design based on a predetermined sample size to construct surrogate models may face challenges of inefficiency or insufficient accuracy. Therefore, an active learning PC-Kriging model for reliability analysis is proposed, which combines the advantages of Polynomial Chaos Expansion for enhancing global approximation accuracy and Kriging for capturing local features. The active learning strategy is utilized to adaptively select the optimal sample points to minimize the training sample size, reducing computational cost of structural performance analysis, and improving analysis efficiency. Further, an active learning PC-Kriging model-driven multi-software co-design framework is constructed. Secondary development of pre-processing and post-processing software is conducted to enable seamless integration of parametric modeling, performance analysis, and post-processing, forming a comprehensive automated analysis workflow. Finally, reliability analysis is performed using a battery pack structure as a case study to verify the efficiency and accuracy of the proposed method.

structural reliability analysis  /  active learning  /  surrogate model  /  PC-Kriging  /  multi-software collaboration
Jiqing Chen, Yuqi Zhang, Fengchong Lan, Yunjiao Zhou, Junfeng Wang. Reliability Analysis Method of Complex Structures Based on Active Learning PC-Kriging Model[J]. Automotive Engineering, 2025 , 47 (2) : 383 -390 . DOI: 10.19562/j.chinasae.qcgc.2025.02.019
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Article Info
doi: 10.19562/j.chinasae.qcgc.2025.02.019
  • Receive Date：2024-06-28
  • Online Date：2025-07-09
  • Published：2025-02-25
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  • Received：2024-06-28
  • Revised：2024-08-29
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    1 School of Mechanical & Automotive Engineering，South China University of Technology，Guangzhou 510640
    2 Guangdong Province Key Laboratory of Automotive，Guangzhou 510640
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2025.02.019
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