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Intelligent meta-model construction and global stochastic sensitivity analysis based on PSO-CNN

STRUCTURES 2022年 43卷 1516-1529页

作者： Dong, Xian Wang, Yadi Wang, Zhan Shijiazhuang Tiedao Univ Sch Civil Engn Shijiazhuang 050043 Hebei Peoples R China South China Univ Technol Dept Civil Engn Guangzhou 510640 Guangdong Peoples R China

A large-scale simulation model is needed for the stochastic sensitivity analysis of structural uncertainty. The finite element method often exceeds the computing power and cannot meet the analysis requirements and computational efficiency of stochastic sensitivity analysis. With the development of artificial intelligence, the calculation efficiency problem can be solved by a combination of the finite element model and a mathematical model, but the accuracy of the analysis results depends on the accuracy of the mathematical model. However, using artificial neural networks to construct mathematical models also has numerous problems, such as falling into local extrema. Thus, to address the computational efficiency and calculation accuracy problems, the particle swarm optimization (PSO)-convolutional neural network (CNN) meta-model is presented in this paper. This model was used to improve the training and prediction accuracy of a small-sample CNN, where the initial learning rate of the CNN was optimized by PSO. Additionally, the PSO was combined with the Adam optimizer to automatically screen the optimal network structure. The global stochastic sensitivity analysis method based on the sobol sensitivity method and the Monte Carlo method was used to quantify the degree of influence of the input parameters on the model output parameters and to analyze the influence trend of the parameter changes on the structural response combined with the positive and negative Spearman's rank correlation coefficients. The accuracy, good adaptability, and extension ability of the PSO-CNN meta-model was verified using the high -dimensional function examples and the structural models. Global stochastic sensitivity analysis was carried out on the joint of an extended end plate. The influence degree of the parameters on the initial rotational stiffness of the joint was determined, and corresponding design suggestions were obtained. These suggestions were consistent with the design specificati

关键词： Global stochastic sensitivity Convolutional neural network Particle swarm optimization algorithm sobol algorithm Artificial intelligence

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