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The structural reliability analysis using explicit neural state functions

机译:使用显式神经状态函数的结构可靠性分析

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The present study considers the problems of stability and reliability of spatial truss susceptible to stability loss from the condition of node snapping. In the reliability analysis of structure, uncertain parameters, such us load magnitudes, cross-sectional area, modulus of elasticity are represented by random variables. Random variables are not correlated. The criterion of structural failure is expressed by the condition of non-exceeding the admissible load multiplier. In the performed analyses explicit form of the random variables function were used. To formulate explicit limit state functions the neural networks is used. In the paper only the time independent component reliability analysis problems are considered. The NUMPRESS software, created at the IFTR PAS, was used in the reliability analysis. The Hasofer-Lind index in conjunction with transformation method in the FORM was used as a reliability measure. The primary research method is the FORM method. In order to verify the correctness of the calculation SORM and Monte Carlo methods are used. The values of reliability index for different descriptions of mathematical model of the structure were determined. The sensitivity of reliability index to the random variables is defined.
机译:本研究考虑了由于节点咬合条件而易遭受稳定性损失的空间桁架的稳定性和可靠性问题。在结构的可靠性分析中,不确定的参数(例如载荷大小,横截面积,弹性模量)由随机变量表示。随机变量不相关。结构破坏的标准由不超过容许载荷乘数的条件表示。在进行的分析中,使用了随机变量函数的显式形式。为了制定明确的极限状态函数,使用了神经网络。在本文中,仅考虑了与时间无关的组件可靠性分析问题。在IFTR PAS上创建的NUMPRESS软件用于可靠性分析。 Hasofer-Lind索引与FORM中的转换方法一起用作可靠性度量。主要研究方法是FORM方法。为了验证计算的正确性,使用了SORM和Monte Carlo方法。确定了针对结构数学模型的不同描述的可靠性指标值。定义了可靠性指标对随机变量的敏感性。

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