Global sensitivity analysis of structural models by active subspace and neural network

Purpose Global 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/approach This 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. Findings Several 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/value The 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.