Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis.

Wang, Zeyu; Shafieezadeh, Abdollah

Metamodel-based approaches to reliability analysis, e.g., adaptive Kriging, are computationally challenged by the complexity of reliability problems, thus limiting the application of these methods to problems that are low-dimensional or not rare. Here, we propose a reliability analysis approach via a deep integration of subset simulation and adaptive kriging (RASA) for an unbiased estimation of failure probabilities of high-dimensional or rare event problems. Concepts of conditional failure probability curves and dynamic learning function are introduced to decompose the original problem to subreliability problems and adaptively identify intermediate failure thresholds of limit state functions corresponding to the subreliability problems. The reliability decomposition and the establishment of target intermediate failure thresholds are guided by the available computational capacity, thus, enabling RASA to control the computational cost associated with the estimation of the intermediate failure thresholds in each subset and consequently to analyze the reliability of medium to high-dimensional problems or rare events. Three numerical examples are investigated as benchmark to explore the performance of the proposed method. Results indicate that the proposed method has high accuracy and has the ability to adjust to available computational resources.

CONDITIONAL probability; KRIGING; COST control; RELIABILITY in engineering; PROBABILITY theory

Structural & Multidisciplinary Optimization, 2021, Vol 64, Issue 2, p649

1615-147X

Academic Journal

10.1007/s00158-021-02864-9