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    易平

    • 教授       硕士生导师
    • 性别:女
    • 毕业院校:大连理工大学
    • 学位:博士
    • 所在单位:土木工程学院
    • 学科:结构工程. 防灾减灾工程及防护工程
    • 办公地点:大连理工大学综合实验三号楼524
    • 联系方式:yiping@dlut.edu.cn
    • 电子邮箱:yiping@dlut.edu.cn

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    An approximate sequential optimization and reliability assessment method for reliability-based design optimization

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    论文类型:期刊论文

    第一作者:Yi, Ping

    通讯作者:Zhu, Z (reprint author), Dalian Univ Technol, Fac Infrastruct Engn, Dalian 116024, Peoples R China.

    合写作者:Zhu, Zuo,Gong, Jinxin

    发表时间:2016-12-01

    发表刊物:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION

    收录刊物:SCIE、EI、Scopus

    卷号:54

    期号:6,SI

    页面范围:1367-1378

    ISSN号:1615-147X

    关键字:Reliability-based design optimization; Sequential optimization and reliability assessment; Minimum performance target point; Efficiency

    摘要:Sequential optimization and reliability assessment (SORA) is one of the most popular decoupled approaches to solve reliability-based design optimization (RBDO) problem because of its efficiency and robustness. In SORA, the double loop structure is decoupled through a serial of cycles of deterministic optimization and reliability assessment. In each cycle, the deterministic optimization and reliability assessment are performed sequentially and the boundaries of violated constraints are shifted to the feasible direction according to the reliability information obtained in the previous cycle. In this paper, based on the concept of SORA, approximate most probable target point (MPTP) and approximate probabilistic performance measure (PPM) are adopted in reliability assessment. In each cycle, the approximate MPTP needs to be reserved, which will be used to obtain new approximate MPTP in the next cycle. There is no need to evaluate the performance function in the deterministic optimization since the approximate PPM and its sensitivity are used to formulate the linear Taylor expansion of the constraint function. One example is used to illustrate that the approximate MPTP will approach the accurate MPTP with the iteration. The design variables and the approximate MPTP converge simultaneously. Numerical results of several examples indicate the proposed method is robust and more efficient than SORA and other common RBDO methods.