Reliability Analysis Method based on Surpport Vector Machines Classification and Adaptive Sampling Strategy

摘要：

For probabilistic design problems with implicit limit state functions encountered in practical application, it is difficult to perform reliability analysis due to the expensive computational cost. In this paper, a new reliability analysis method which applies support vector machine classification(SVM-C) and adaptive sampling strategy is proposed to improve the efficiency. The SVM-C constructs a model defining the boundary of failure regions which classifies samples as safe or failed using SVM-C, then this model is used to replace the true limit state function,thus reducing the computational cost. The adaptive sampling strategy is applied to select samples along the constraint boundaries. It can also improves the efficiency of the proposed method. In the end, a probability analysis example is presented to prove the feasible and efficient of the proposed method.

关键词： Support Vector Machine Classification Adaptive Sampling Strategy Reliability Analysis

作者: Hongyan Hao Haobo Qiu Zhenzhong Chen Huadi Xiong

作者单位: The State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Sc The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of S

会议类型: 国际会议

会议名称: 第三届产品开发与可靠性进展国际会议

会议地点: 武汉

会议语种:英文

页码: 212-217

在线出版日期: 2012-07-29（万方平台首次上网日期，不代表论文的发表时间）