Combined relevance vector machine technique and subset simulation importance sampling for structural reliability

DOI10.1016/J.APM.2022.09.010zbMath1505.65007OpenAlexW4295242104WikidataQ114208483 ScholiaQ114208483MaRDI QIDQ2110833

Chong Peng, Bin Xie, Yanzhong Wang

Publication date: 23 December 2022

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2022.09.010

zbMATH Keywords

reliability analysisrelevance vector machinesmall failure probabilityK-means clustering algorithmsubset simulation importance sampling

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items (1)

A new Bayesian probabilistic integration framework for hybrid uncertainty propagation

Uses Software

• AS 136
• AK-MCS

Cites Work

• Algorithm AS 136: A K-Means Clustering Algorithm
• Active learning relevant vector machine for reliability analysis
• A Laplace asymptotic integral-based reliability analysis method combined with artificial neural network
• Adaptive reliability analysis based on a support vector machine and its application to rock engineering
• An adaptive multiple-kriging-surrogate method for time-dependent reliability analysis
• 10.1162/15324430152748236

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