For efficiently and accurately estimating the failure probability and the further sensitivity index of the high-dimension structures, a novel AS-AK-MCS method is proposed in this work. This method fully employs the merits of the active subspace-based dimension reduction technique, the active learning (AL) Kriging surrogate model, and the Monte Carlo simulation. In the proposed method, the intractable gradient information needed by the active subspace method is obtained by a crude Kriging model with initial training sample points. In the construction of the crude Kriging model, the proposed trend model selection criterion reduces the man-made error. Then the active subspace method converts the reliability analysis from the original high-dimension space into the low-dimension subspace, which facilities constructing an AL-Kriging model and also avoids the tricky “curse of dimension” problem. The state-of-the-art U learning function is applied as the points adding criterion in the active subspace. In order to demonstrate the effectivity and versatility of the proposed method, three representative examples including the linear/nonlinear and the explicit/implicit performance functions are studied for estimating the failure probability and the failure probability-based sensitivity index. Finally, the proposed method is applied to the reliability and sensitivity analysis of a composite radome structure.
PurposeGlobal sensitivity can measure the influence of input variables on model responses and is of positive significance for the improvement design of structural systems. This work aims to study the global sensitivity of structural models by combining the active subspace theory and neural network.Design/methodology/approachThis study aims to improve the efficiency of global sensitivity analysis for high-dimensional structural systems, a novel method based on active subspace and surrogate model is proposed. Active subspace can reduce the dimension of input variables, and an adaptive scaling strategy is proposed to improve the accuracy in finding the active subspace. The uncertainty propagation of active variables and model response is performed through the artificial neural network. Then the global sensitivity analysis is carried out.FindingsSeveral examples are studied by using the Monte Carlo simulation method and the proposed method. Comparison of the results shows that the proposed method has preferable accuracy and low computational cost.Originality/valueThe proposed method provides a practicable tool for the variance-based sensitivity analysis of structural systems. Apart from sensitivity analysis, the method can be also extended for use in other fields relating to uncertainty propagation.
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