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http://dx.doi.org/10.7734/COSEIK.2017.30.1.87

Reliability Analysis Using Parametric and Nonparametric Input Modeling Methods  

Kang, Young-Jin (School of Mechanical Engineering, Pusan Nat'l Univ.)
Hong, Jimin (School of Mechanical Engineering, Pusan Nat'l Univ.)
Lim, O-Kaung (School of Mechanical Engineering, Pusan Nat'l Univ.)
Noh, Yoojeong (School of Mechanical Engineering, Pusan Nat'l Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.30, no.1, 2017 , pp. 87-94 More about this Journal
Abstract
Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.
Keywords
kernel density estimation; nonparametric statistical modeling; parametric statistical modeling; reliability analysis; sequential statistical modeling;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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