Active learning relevant vector machine for reliability analysis

2021 ◽

Vol 1043 (5) ◽

pp. 052057

Author(s):

Xin Cheng ◽

Xufeng Yang

Keyword(s):

Fatigue Strength ◽

Active Learning ◽

Reliability Analysis ◽

Kriging Model ◽

Strength Based

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A coupled subset simulation and active learning kriging reliability analysis method for rare failure events

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-019-02326-3 ◽

2019 ◽

Vol 60 (6) ◽

pp. 2325-2341 ◽

Cited By ~ 3

Author(s):

Chunyan Ling ◽

Zhenzhou Lu ◽

Kaixuan Feng ◽

Xiaobo Zhang

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Analysis Method ◽

Subset Simulation

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A new active learning method based on the learning function U of the AK-MCS reliability analysis method

Engineering Structures ◽

10.1016/j.engstruct.2017.06.038 ◽

2017 ◽

Vol 148 ◽

pp. 185-194 ◽

Cited By ~ 21

Author(s):

Zheng Peijuan ◽

Wang Chien Ming ◽

Zong Zhouhong ◽

Wang Liqi

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Learning Method ◽

Analysis Method ◽

Learning Function ◽

Active Learning Method

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An efficient hybrid reliability analysis method based on active learning Kriging model and multimodal‐optimization‐based importance sampling

International Journal for Numerical Methods in Engineering ◽

10.1002/nme.6847 ◽

2021 ◽

Author(s):

Tai Wang ◽

Xufeng Yang ◽

Mi Caiying

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Importance Sampling ◽

Kriging Model ◽

Multimodal Optimization ◽

Analysis Method ◽

Hybrid Reliability

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REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis

Reliability Engineering & System Safety ◽

10.1016/j.ress.2019.01.014 ◽

2019 ◽

Vol 185 ◽

pp. 440-454 ◽

Cited By ~ 46

Author(s):

Xufang Zhang ◽

Lei Wang ◽

John Dalsgaard Sørensen

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Structural Reliability ◽

Surrogate Models ◽

Learning Function ◽

Structural Reliability Analysis ◽

Adaptive Kriging ◽

Active Learning Function

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MEAK-MCS: Metamodel Error Measure Function based Active Learning Kriging with Monte Carlo Simulation for Reliability Analysis

2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD) ◽

10.1109/cscwd.2019.8791933 ◽

2019 ◽

Author(s):

Jinhao Zhang ◽

Mi Xiao ◽

Liang Gao ◽

Yan Zhang

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Active Learning ◽

Reliability Analysis ◽

Error Measure ◽

Measure Function

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An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis

Probabilistic Engineering Mechanics ◽

10.1016/j.probengmech.2020.103054 ◽

2020 ◽

Vol 60 ◽

pp. 103054 ◽

Cited By ~ 4

Author(s):

Qing Guo ◽

Yongshou Liu ◽

Bingqian Chen ◽

Yuzhen Zhao

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Importance Sampling ◽

Sampling Method ◽

Kriging Model ◽

Importance Sampling Method

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Structural reliability analysis under evidence theory using the active learning kriging model

Engineering Optimization ◽

10.1080/0305215x.2016.1277063 ◽

2017 ◽

Vol 49 (11) ◽

pp. 1922-1938 ◽

Cited By ~ 14

Author(s):

Xufeng Yang ◽

Yongshou Liu ◽

Panke Ma

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Structural Reliability ◽

Evidence Theory ◽

Kriging Model ◽

Structural Reliability Analysis

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Tunnel face reliability analysis using active learning Kriging model—Case of a two-layer soils

Journal of Central South University ◽

10.1007/s11771-019-4129-0 ◽

2019 ◽

Vol 26 (7) ◽

pp. 1735-1746

Author(s):

Tian-zheng Li ◽

Daniel Dias

Keyword(s):

Active Learning ◽

Reliability Analysis ◽

Kriging Model ◽

Tunnel Face ◽

Model Case

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An active learning method combining deep neural network and weighted sampling for structural reliability analysis

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2020.106684 ◽

2020 ◽

Vol 140 ◽

pp. 106684 ◽

Cited By ~ 3

Author(s):

Zhengliang Xiang ◽

Jiahui Chen ◽

Yuequan Bao ◽

Hui Li

Keyword(s):

Neural Network ◽

Active Learning ◽

Reliability Analysis ◽

Deep Neural Network ◽

Structural Reliability ◽

Learning Method ◽

Structural Reliability Analysis ◽

Active Learning Method

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Active learning relevant vector machine for reliability analysis

Reliability Analysis of Wheel fatigue strength based on active learning Kriging Model

A coupled subset simulation and active learning kriging reliability analysis method for rare failure events

A new active learning method based on the learning function U of the AK-MCS reliability analysis method

An efficient hybrid reliability analysis method based on active learning Kriging model and multimodal‐optimization‐based importance sampling

REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis

MEAK-MCS: Metamodel Error Measure Function based Active Learning Kriging with Monte Carlo Simulation for Reliability Analysis

An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis

Structural reliability analysis under evidence theory using the active learning kriging model

Tunnel face reliability analysis using active learning Kriging model—Case of a two-layer soils

An active learning method combining deep neural network and weighted sampling for structural reliability analysis

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