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首页> 外文会议>The 7th China-Japan-Korea joint symposium on optimization of structural and mechanical systems >A New Hybrid Simulation Approach to Structural Reliability Analysis Using Uniform Design, ANN Meta-model, Genetic Algorithms and FORM
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A New Hybrid Simulation Approach to Structural Reliability Analysis Using Uniform Design, ANN Meta-model, Genetic Algorithms and FORM

机译：基于均匀设计，ANN元模型，遗传算法和FORM的结构可靠性分析的混合仿真新方法

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摘要

A new hybrid simulation method for structural reliability analysis is proposed which combines uniform design (UD) technique, artificial neural network (ANN) based meta-model, genetic algorithm (GA) and first order reliability method (FORM). The uniform design instead of classical central composite design (CCD) or orthogonal array design (OAD) is applied to choose experiment points in space of basic random variables aimed to minimize the number of simulation and to fill the space more uniformly.A BP-ANN based meta-model is used as a smart response surface surrogate to the original implicit limit state function in the global random variable space, with the UD experimental points as input training data sets of the ANN.Due to the highly nonlinear nature of ANN-based smart response surface, the Genetic algorithm (GA) incorporating FORM is employed to search for the global design point or most probable point (MPP) of failure to avoid fall into the local optimal solutions. To implement deterministic finite element analysis in the evaluation of the limit state function and finite element response sensitivity, the proposed approach is programmed in MATLAB by calling and integrating the commercial finite element analysis program ANSYS. Three numerical examples are provided to demonstrate the accuracy, efficiency and applicability of the proposed method by contrasting the new approach with the classical computational reliability methods such as Monte Carlo simulation (MCS), first order reliability method (FORM), and response surface method (RSM).
机译：提出了一种新的混合可靠性分析方法，该方法结合了统一设计（UD）技术，基于人工神经网络的元模型，遗传算法（GA）和一阶可靠性方法（FORM）。 BP-ANN采用统一设计代替经典的中央复合设计（CCD）或正交阵列设计（OAD）来选择基本随机变量空间中的实验点，以最大程度地减少模拟次数并更均匀地填充空间。基于UD的高度非线性本质，基于UD的实验模型作为ANN的输入训练数据集，基于EM的元模型被用作全局随机变量空间中原始隐式极限状态函数的智能响应面替代品。在智能响应面的基础上，结合FORM的遗传算法（GA）用于搜索整体设计点或最可能发生故障的点（MPP），以避免陷入局部最优解。为了在极限状态函数的评估和有限元响应灵敏度的评估中执行确定性有限元分析，通过调用和集成商业有限元分析程序ANSYS，在MATLAB中对提出的方法进行了编程。通过将新方法与经典计算可靠性方法（例如蒙特卡罗模拟（MCS），一阶可靠性方法（FORM）和响应面方法（））进行对比，提供了三个数值示例来说明该方法的准确性，效率和适用性（ RSM）。

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