Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

Abstract

Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 11 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.