Statistical data expansion using Kriging for probabilistic capacity factor calibration

Abstract

Abstract This study proposes a new reliability-based capacity factor calibration method based on the EN 1990 Annex D method in combination with Kriging for a statistical data expansion. The original EN 1990 Annex D method has been widely used in partial safety factor or capacity factor calibrations in international structural design standards by rigorously considering the modelling error of the design equations in comparison with real experimental data. However, as the number of experimental data is often limited in practical situations, large statistical uncertainty needs to be incorporated in the safety factor calibration process, and the calibrated partial safety factors or capacity factors have large variations. In the proposed method, Kriging, a data-driven nonlinear interpolation method, is utilised to statistically expand the experimental database used for modelling error estimation when experimental data are limited. To make the calculation rigorous, the Kriging error of the statistically expanded data is also estimated and incorporated into the proposed framework. The proposed method is demonstrated through two numerical examples and three real world structural design examples including the shear strength of reinforced concrete beams and the axial resistance of concrete-filled steel tubular stub columns with circular and rectangular sections, each of which has more than 300 experimental data for verification. The results show that the proposed method always improves the calibration results by statistical data expansion, which does not need any additional physical costs for experiments.