Classification approach for reliability analysis with stochastic finite-element modeling
Classification approach for reliability analysis with stochastic finite-element modeling
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DOI：
10.1061/(asce)0733-9445(2003)129:8(1141
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发表时间：
2003-08-01
影响因子：
4.1
通讯作者：
Alvarez, DA
中科院分区：
文献类型：
--
作者：
Hurtado, JE;Alvarez, DA
结构系统可靠性的评估越来越多地根据机械性能和载荷的空间波动进行评估。这导致了称为随机有限元 (SFE) 的详细概率建模。本文提出了一种与 SFE 模型可靠性分析主流方法不同的方法。不同之处在于可靠性问题被视为分类任务而不是积分的计算。为此，使用了分类的核方法，这是模式识别、图像分析和其他领域深入研究的对象。开发了需要最少数量的极限状态评估的贪婪顺序过程。该算法基于支持向量的关键概念，保证只需要评估最接近决策规则的点。数值例子表明，该算法能够以最少的有限元求解器调用次数和快速计算的方式获得 SFE 模型失效概率的高精度近似值。
The assessment of the reliability of structural systems is increasingly being estimated with regard to the spatial fluctuation of the mechanical properties as well as loads. This leads to a detailed probabilistic modeling known as stochastic finite elements (SFE). In this paper an approach that departs from the main stream of methods for the reliability analysis of SFE models is proposed. The difference lies in that the reliability problem is treated as a classification task and not as the computation of an integral. To this purpose use is made of a kernel method for classification, which is the object of intensive research in pattern recognition, image analysis, and other fields. A greedy sequential procedure requiring a minimal number of limit state evaluations is developed. The algorithm is based on the key concept of support vectors, which guarantee that only the points closest to the decision rule need to be evaluated. The numerical examples show that this algorithm allows obtaining a highly accurate approximation of the failure probability of SFE models with a minimal number of calls of the finite element solver and also a fast computation.