A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis

Abstract

Current surrogate modeling methods for time-dependent reliability analysis implement a double-loop procedure, with the computation of extreme value response in the outer loop and optimization in the inner loop. The computational effort of the double-loop procedure is quite high even though improvements have been made to improve the efficiency of the inner loop. This paper proposes a single-loop Kriging (SILK) surrogate modeling method for time-dependent reliability analysis. The optimization loop used in current methods is completely removed in the proposed method. A single surrogate model is built for the purpose of time-dependent reliability assessment. Training points of random variables and over time are generated at the same level instead of at two separate levels. The surrogate model is refined adaptively based on a learning function modified from time-independent reliability analysis and a newly developed convergence criterion. Strategies for building the surrogate model are investigated for problems with and without stochastic processes. Results of three numerical examples show that the proposed single-loop procedure significantly increases the efficiency of time-dependent reliability analysis without sacrificing the accuracy.

Resilience Assessment Based on Time-Dependent System Reliability Analysis

“…Most of the reported system reliability analysis methods rely on the first-order reliability method (FORM). In this paper, to remove the limitation of FORM and yet be computationally efficient, a recently developed single-loop Kriging (SILK) surrogate modeling method is employed and extended for time-dependent system reliability analysis [33]. Note that failure sequences and brittle failure events [34] are important issues for time-dependent system reliability analysis.…”

“…In SILK [33], a single surrogate modelĜ ¼ĝðX; Y; tÞ is built using the Kriging surrogate modeling method for time-dependent reliability analysis. An initial surrogate modelĜ ¼ĝðX; Y; tÞ is built.…”

“…During iterations of the surrogate model training, the quality of the Kriging surrogate model is checked using the following convergence criterion [33]:…”

A New Method for Reliability‐Based Sensitivity Analysis of Dynamic Random Systems

“…As the exciting force of the dynamic random system is a time-dependent random process, it is computationally expensive to obtain the time-varying reliability value in the time domain. Hu [16,17] used an extreme value response method to solve the time-dependent reliability problems, and Wang et al [18] used this method in kinematic reliability analysis.…”

A Partial Safety Factor Method for System Reliability Prediction With Outsourced Components

“…including Kriging Surrogate Modeling [21,22], Linear Regression (LR) [23], Artificial Neural Network (ANN) [24], and Support Vector Machines (SVM) [25,26].…”