NDLI: Support vector machine for structural reliability analysis

Content Provider	Springer Nature Link
Author	Lü, Zhen zhou Li, Hong shuang Yue, Zhu feng
Copyright Year	2006
Abstract	Support vector machine (SVM) was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method (FORM) and the Monte Carlo simulation (MCS). As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method (RSM) and the artificial neural network (ANN), which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the “curse of dimensionality”. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis (usually finite element analysis), and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.
Ending Page	1303
Page Count	9
Starting Page	1295
File Format	PDF
ISSN	02534827
e-ISSN	15732754
Journal	Applied Mathematics and Mechanics
Issue Number	10
Volume Number	27
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	structural reliability support vector machine Reliability, availability, maintenance, inspection implicit performance function Mechanics Applications of Mathematics Mathematical Modeling and Industrial Mathematics Reliability and life testing
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Mechanics of Materials Mechanical Engineering

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