Kaixuan Feng

Orcid: 0000-0002-4325-6992

According to our database1, Kaixuan Feng authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A Single-Loop Fuzzy Simulation-Based Adaptive Kriging Method for Estimating Time-Dependent Failure Possibility.
Int. J. Fuzzy Syst., November, 2024

A double-loop Kriging model algorithm combined with importance sampling for time-dependent reliability analysis.
Eng. Comput., June, 2024

Metamodel-Based Directional Importance Sampling for Structural Reliability Analysis.
IEEE Trans. Reliab., March, 2024

Two-Phase Adaptive Kriging Model Based Importance Sampling Method for Estimating Time-Dependent Failure Probability.
IEEE Trans. Reliab., March, 2024

2023
Survival signature based robust redundancy allocation under imprecise probability.
Reliab. Eng. Syst. Saf., November, 2023

Novel Kriging based learning function for system reliability analysis with correlated failure modes.
Reliab. Eng. Syst. Saf., November, 2023

An efficient hierarchical fuzzy simulation method for estimating failure possibility.
Eng. Comput., October, 2023

Reliability analysis of bending fatigue life of hydraulic pipeline.
Reliab. Eng. Syst. Saf., 2023

2022
An efficient trajectory sampling design method for elementary effect based global sensitivity analysis.
Commun. Stat. Simul. Comput., 2022

2021
Improved chance index and its solutions for quantifying the structural safety degree under twofold random uncertainty.
Reliab. Eng. Syst. Saf., 2021

Fuzzy importance sampling method for estimating failure possibility.
Fuzzy Sets Syst., 2021

2020
Two Efficient AK-Based Global Reliability Sensitivity Methods by Elaborative Combination of Bayes' Theorem and the Law of Total Expectation in the Successive Intervals Without Overlapping.
IEEE Trans. Reliab., 2020

A novel dual-stage adaptive Kriging method for profust reliability analysis.
J. Comput. Phys., 2020

2019
An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation.
Reliab. Eng. Syst. Saf., 2019


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