结构非概率可靠性分析的支持向量机分类方法

SUPPORT VECTOR CLASSIFICATION FOR STRUCTURAL NON-PROBABILISTIC RELIABILITY ANALYSIS

利用支持向量机分类技术解决隐式极限状态结构的非概率可靠性问题。基于未确知信息的分段描述模型,设计了训练样本抽取策略,将基本变量区域中的样本等效转化为标准区间变量域中的样本,统一了尺度,有效保证了支持向量机训练的稳定性,并使蒙特卡洛模拟更易实现,有效解决了隐式极限状态结构的非概率可靠性分析问题。通过2个算例对文中方法的精度和可行性进行了验证。

The classification technology of support vector machine（SVM） was introduced to analyze the non-probabilistic reliability of structures with implicit limit state function.Based on the fragment description model of unascertained information,the training data sampling method was proposed.The sample data in basic variable range was transformed to those in normal interval variable scale,and the dimensions of training samples were unified.So the stability of SVM could be guaranteed and Monte Carlo simulation（MCS） became easier to perform.The problem of structural non-probabilistic reliability with implicit limit state function was solved.The accuracy and feasibility of this methodology were proved through two given examples.