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.

这项研究提出了一种新的基于可靠性的容量因子校准方法，该方法基于EN 1990 Annex D方法与Kriging相结合，用于统计数据扩展。最初的EN 1990 Annex D方法通过与实际实验数据进行比较仔细考虑设计方程的建模误差，已广泛用于国际结构设计标准中的部分安全系数或容量系数校准中。然而，由于在实际情况下实验数据的数量通常是有限的，因此在安全系数校准过程中需要纳入较大的统计不确定性，并且校准后的部分安全系数或容量系数会有较大的差异。在提出的方法Kriging（一种数据驱动的非线性插值方法）中，当实验数据受到限制时，可用于统计地扩展用于建模误差估计的实验数据库。为了使计算更加严格，还估算了统计扩展数据的Kriging误差并将其纳入建议的框架中。通过两个数值示例和三个实际结构设计示例（包括钢筋混凝土梁的抗剪强度和圆形截面和矩形截面的钢管混凝土短柱的轴向阻力）演示了所提出的方法，每个试验都进行了300多次实验数据进行验证。结果表明，所提出的方法总是通过统计数据扩展来改善校准结果，而无需任何额外的物理成本进行实验。