کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5019552 | 1468213 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
LIF: A new Kriging based learning function and its application to structural reliability analysis
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The main task of structural reliability analysis is to estimate failure probability of a studied structure taking randomness of input variables into account. To consider structural behavior practically, numerical models become more and more complicated and time-consuming, which increases the difficulty of reliability analysis. Therefore, sequential strategies of design of experiment (DoE) are raised. In this research, a new learning function, named least improvement function (LIF), is proposed to update DoE of Kriging based reliability analysis method. LIF values how much the accuracy of estimated failure probability will be improved if adding a given point into DoE. It takes both statistical information provided by the Kriging model and the joint probability density function of input variables into account, which is the most important difference from the existing learning functions. Maximum point of LIF is approximately determined with Markov Chain Monte Carlo(MCMC) simulation. A new reliability analysis method is developed based on the Kriging model, in which LIF, MCMC and Monte Carlo(MC) simulation are employed. Three examples are analyzed. Results show that LIF and the new method proposed in this research are very efficient when dealing with nonlinear performance function, small probability, complicated limit state and engineering problems with high dimension.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Reliability Engineering & System Safety - Volume 157, January 2017, Pages 152-165
Journal: Reliability Engineering & System Safety - Volume 157, January 2017, Pages 152-165
نویسندگان
Zhili Sun, Jian Wang, Rui Li, Cao Tong,