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Structural optimization based on reliability index mapping functions

机译:基于可靠性指标映射功能的结构优化

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摘要

Reliability Based Optimization (RBO) is often computationally intensive because the coupling of reliability evaluation and cost minimization. The mean first order reliability method (MFORM) is efficient for reliability evaluation, but it may not accurate. Nevertheless, the accurate reliability index is often highly correlated with the MFORM reliability index. In this study, we suggest that one can determine the relationship between the MFORM reliability index and the accurate reliability index through a regression analysis, which is called the reliability index mapping function. Based on the reliability index mapping function, an equivalent MFORM target reliability index can be determined such that MFORM can be used for reliability evaluation in RBO. Since the computational work involved in MFORM is minimal, RBO based on reliability index mapping functions is efficient. The reliability index mapping function determined via a regression analysis may only reflect the approximate relationship between the accurate reliability index and the MFORM reliability index. How to consider uncertainties in reliability index mapping functions in RBO is also described in this paper.
机译:基于可靠性的优化(RBO)通常是计算密集型的,因为可靠性评估和成本最小化相结合。平均一阶可靠性方法(MFORM)可有效地进行可靠性评估,但可能不准确。但是,准确的可靠性指标通常与MFORM可靠性指标高度相关。在这项研究中,我们建议人们可以通过回归分析来确定MFORM可靠性指标和准确的可靠性指标之间的关系,这称为可靠性指标映射函数。基于可靠性指标映射函数,可以确定等效的MFORM目标可靠性指标,以便可以将MFORM用于RBO中的可靠性评估。由于MFORM涉及的计算工作量很小,因此基于可靠性指标映射功能的RBO是高效的。通过回归分析确定的可靠性指标映射函数可能仅反映准确的可靠性指标和MFORM可靠性指标之间的近似关系。本文还介绍了如何考虑RBO中可靠性指标映射函数的不确定性。

著录项

  • 来源
  • 会议地点 Munchen(DE)
  • 作者

    Wilson H. Tang; J. Zhang;

  • 作者单位

    Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technologv, Hong Kong SAR, China;

    Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technologv, Hong Kong SAR, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统工程;
  • 关键词

  • 入库时间 2022-08-26 14:23:16

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