Advanced Sobol sensitivity analysis of a 1:4-scale prestressed concrete containment vessel using an ANN-based surrogate model

Abstract

The safety of nuclear power plants can be enhanced by performing sensitivity analyses to identify and evaluate factors that significantly impact their structural integrity. In this study, an artificial neural network-based surrogate model was developed using an experimentally validated finite element model. A Sobol sensitivity analysis was performed using this model to quantitatively assess the impact of material uncertainty on the internal pressure–displacement behavior of containment buildings at various internal pressure levels. The results indicate that the compressive strength of concrete and the prestressing force in the hoop direction critically influence the behavior of containment buildings, with their importance varying according to the internal pressure level. Furthermore, the proposed surrogate model can efficiently substitute for expensive and complex finite element analyses, enabling effective exploration of the multidimensional uncertainty space and quantitative assessment of the major factors affecting structural behavior. The process presented herein facilitates the quantification of factors under various uncertainty scenarios.