Machine learning-based methods in structural reliability analysis: A review

•A review of the machine learning-based structural reliability analysis methods is presented.•Artificial neural networks-based structural reliability analysis methods are reviewed.•Support vector machines-based structural reliability analysis methods are reviewed.•The application of Bayes’ theorem f...

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Veröffentlicht in:	Reliability engineering & system safety Jg. 219; S. 108223
Hauptverfasser:	Saraygord Afshari, Sajad, Enayatollahi, Fatemeh, Xu, Xiangyang, Liang, Xihui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Barking Elsevier Ltd 01.03.2022 Elsevier BV
Schlagworte:	Artificial neural networks Bayesian analysis Computer applications Computer simulation Importance sampling Kriging estimation Learning algorithms Learning theory Limit states Literature reviews Machine learning Mathematical models Mechanical engineering Monte Carlo simulation Neural networks Pattern recognition Reliability analysis Reliability engineering Response surface method State-of-the-art reviews Structural analysis Structural reliability Support vector machines Surrogate modeling CDF PoF LS MCMC Surrogate modeling MPP NBC MLP SuS DBN MLS RBF Monte carlo simulation DOE RBDO LSF ML Kriging estimation EIF Artificial neural networks AI AK IS AL PSO RSM SORM ERF Response surface method Bayesian analysis FPF NN SVD ANN ERM SVC FOSM SRA EFF NPF BI Structural reliability GRNN SVM BN SVR BR NLO PDF LHS WNN BMC GA BDLM MSE BUS AKM FORM LIF kNN Support vector machines GRENN LF MCS
ISSN:	0951-8320, 1879-0836
Online-Zugang:	Volltext
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