Recommendations for Active-Learning Kriging Reliability Analysis of Bridge Structures

Abstract

Active-learning Kriging (AK) was developed as a surrogate-aided reliability technique to address the need for efficient reliability estimation when assessing complex limit states. The results of AK analyses are sensitive to the choice of the regression function, correlation function, learning function and associated stopping criteria, and reliability estimation technique, with unique sets of these input parameters referred to as AK configurations. For the reliable use of AK analysis in bridge reliability assessment, recommendations regarding the best-performing AK configurations are needed to balance the desired accuracy-to-efficiency of the simulation. The objective of this study was to recommend sets of AK configurations for the reliability analysis of reinforced-concrete bridge girders and piers that can be readily used by engineers to perform AK analysis for bridge design optimization and assessment. An extensive parametric analysis, using 432 unique AK configurations and over 3,000 AK analyses, was performed, combined with the application of a comprehensive metric system to recommend the top five best-performing AK configurations for bridge analysis based on the root mean square error, the absolute average error, the degree of consistency, and total number of training points.