HALK: A hybrid active-learning Kriging approach and its applications for structural reliability analysis

The paper presents a hybrid active-learning approach for structural reliability analysis via adaptive Kriging surrogate models. The quasi first-order reliability method is first proposed for characteristic truncation point of a structural performance function. This is used to define a truncation boundary via the joint probability distribution function of input random variables. To reduce simulation time for new training samples, a U-function based criterion is further implemented to refine the candidate sample set. Since the reliability-based expected improvement function and U functions are combined together to evaluate new training samples, it finalizes a hybrid active-learning Kriging (HALK) to develop adaptive surrogate models for the structural reliability analysis. Several numerical examples are presented to demonstrate potential applications of the proposed HALK algorithm. Compared to benchmark results provided by the brutal force Monte-Carlo simulation method, the effectiveness of the HALK approach has been justified by dealing with various structural reliability problems.

Automated summary

The paper introduces a hybrid active-learning approach using adaptive Kriging surrogate models for structural reliability analysis. It combines the quasi first-order reliability method and a U-function based criterion to optimize training sample efficiency, resulting in the HALK algorithm for developing adaptive surrogate models. Numerical examples demonstrate the effectiveness of the HALK algorithm in handling structural reliability problems.