دوره 7، شماره 1 - ( 10-1395 )                   جلد 7 شماره 1 صفحات 93-107 | برگشت به فهرست نسخه ها


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Shayanfar M A, Barkhordari M A, Roudak M A. AN ADAPTIVE IMPORTANCE SAMPLING-BASED ALGORITHM USING THE FIRST-ORDER METHOD FOR STRUCTURAL RELIABILITY. International Journal of Optimization in Civil Engineering. 2017; 7 (1) :93-107
URL: http://ijoce.iust.ac.ir/article-1-286-fa.html
AN ADAPTIVE IMPORTANCE SAMPLING-BASED ALGORITHM USING THE FIRST-ORDER METHOD FOR STRUCTURAL RELIABILITY. دانشگاه علم و صنعت ایران. 1395; 7 (1) :93-107

URL: http://ijoce.iust.ac.ir/article-1-286-fa.html


چکیده:   (1156 مشاهده)

Monte Carlo simulation (MCS) is a useful tool for computation of probability of failure in reliability analysis. However, the large number of samples, often required for acceptable accuracy, makes it time-consuming. Importance sampling is a method on the basis of MCS which has been proposed to reduce the computational time of MCS. In this paper, a new adaptive importance sampling-based algorithm applying the concepts of first-order reliability method (FORM) and using (1) a new simple technique to select an appropriate initial point as the location of design point, (2) a new criterion to update this design point in each iteration and (3) a new sampling density function, is proposed to reduce the number of deterministic analyses. Besides, although this algorithm works with the position of design point, it does not need any extra knowledge and updates this position based on previous generated results. Through illustrative examples, commonly used in the literature to test the performance of new algorithms, it will be shown that the proposed method needs fewer number of limit state function (LSF) evaluations.

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نوع مطالعه: پژوهشي | موضوع مقاله: Optimal design
دریافت: ۱۳۹۵/۵/۱۵ | پذیرش: ۱۳۹۵/۵/۱۵ | انتشار: ۱۳۹۵/۵/۱۵

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