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FEM-NN-MCS-Based Estimation of Stress Concentration Factors in Reliability Analysis

机译：基于FEM-NN-MCS的可靠性分析中应力集中因子的估计

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The stress concentration factors (SCF) is one of the most important parameters in reliability analysis of mechanical structures, because the precision of the SCF predicted would affect the computation results of reliability analysis directly. Generally, the SCF is difficulty to be obtained adopting theoretical method, and at the present time some experimental methods are usually adopted. Although the numerical solution of the SCF can be obtained accurately applying experimental methods, its explicit expression between the basic random variables and the SCF can't be given directly and which will bring some difficulties for the further reliability analysis. As an alternative approach, the finite element method (FEM) is used to gain the SCF database, and neural network (NN) has been developed to simulate the explicit expression of the SCF based on the database obtained by FEM. Moreover, the trained NN is used to estimate the SCF replacing the complicated computation of FEM in the corresponding reliability analysis.
机译：压力集中因子（SCF）是机械结构可靠性分析中最重要的参数之一，因为SCF预测的精度会直接影响可靠性分析的计算结果。通常，采用理论方法难以获得SCF，并且目前通常采用一些实验方法。尽管可以准确地施加SCF的数值解，但是在基本随机变量和SCF之间的明确表达不能直接给出，并且可以为进一步的可靠性分析带来一些困难。作为替代方法，使用有限元方法（FEM）来获得SCF数据库，并且已经开发了神经网络（NN）来基于由FEM获得的数据库来模拟SCF的显式表达式。此外，培训的NN用于估计在相应的可靠性分析中替换FEM复杂计算的SCF。

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