Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1537
Title: Reliability prediction through guided tail modeling using support vector machines
Authors: Acar, Erdem
143688
Keywords: Guided simulations
high reliability
support vector machines
tail modeling
Issue Date: Dec-2013
Publisher: Sage Publications Ltd
Source: Acar, E. (2013). Reliability prediction through guided tail modeling using support vector machines. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227(12), 2780-2794.
Abstract: Reliability prediction of highly safe mechanical systems can be performed using classical tail modeling. Classical tail modeling is based on performing a relatively small number of limit-state evaluations through a sampling scheme and then fitting a tail model to the tail part of the data. However, the limit-state calculations that do not belong to the tail part are discarded, so majority of limit-state evaluations are wasted. Guided tail modeling, proposed earlier by the author, can provide a remedy through guidance of the limit-state function calculations toward the tail region. In the original guided tail modeling, the guidance is achieved through a procedure based on threshold estimation using univariate dimension reduction and extended generalized lambda distribution and tail region approximation using univariate dimension reduction. This article proposes a new guided tail modeling technique that utilizes support vector machines. In the proposed method, named guided tail modeling with support vector machines (GTM-SVM), the threshold estimation is still performed using univariate dimension reduction and extended generalized lambda distribution, while the tail region approximation is based on support vector machines. The performance of guided tail modeling with support vector machines is tested with mathematical example problems as well as structural mechanics problems with varying number of variables. GTM-SVM is found to be more accurate than both guided tail modeling and classical tail modeling for low-dimensional problems. For high-dimensional problems, on the other hand, the original guided tail modeling is found to be more accurate than guided tail modeling with support vector machines, which is superior to classical tail modeling.
URI: https://doi.org/10.1177/0954406213479846
https://hdl.handle.net/20.500.11851/1537
ISSN: 0954-4062
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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