郝鹏

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:Professor

其他任职:工程力学系主任

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:力学与航空航天学院

学科:固体力学. 航空航天力学与工程. 计算力学. 工程力学

联系方式:haopeng@dlut.edu.cn

电子邮箱:haopeng@dlut.edu.cn

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An augmented step size adjustment method for the performance measure approach: Toward general structural reliability-based design optimization

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论文类型:期刊论文

发表时间:2019-01-01

发表刊物:STRUCTURAL SAFETY

收录刊物:SCIE、EI

卷号:80

页面范围:32-45

ISSN号:0167-4730

关键字:Reliability-based design optimization; Performance measure approach; Inverse reliability analysis, First-order reliability method; Augmented step size adjustment method

摘要:Heavy computational burden has been one of the largest barriers to the application of reliability-based design optimization (RBDO) in real-world structures. The key difficulty of RBDO lies in how to perform reliability analysis efficiently and robustly. In performance measure approach (PMA)-based REDO, the reliability analysis process searches for the minimum performance target point (MPTP) with the target reliability index in standard normal space. Many methods have been proposed to improve the efficiency and robustness of the PMA. However, these methods may face the convergence problem for highly nonlinear constraint functions; or high computational cost for weakly nonlinear ones. More importantly, most existing methods are very sensitive to the selection of algorithm parameters. In this paper, an augmented step size adjustment (ASSA) method is proposed to boost the iterative process in terms of both efficiency and robustness. According to the relative positions of the direction vector and negative gradient direction and the angle between them at each iterative point, a fire-new strategy is established to identify the oscillation during the iterative process and define the iterative step size. Seven inverse reliability analysis problems and three REDO benchmarks are used to validate the performance of the ASSA method. The results indicate that the ASSA method has wide applicability for nonlinear constraint functions and achieves efficient and robust performance. Furthermore, the results demonstrate that the ASSA method can be regarded as a reliable and effective method for addressing PMA-based REDO problems.