.png){kind=icon}
返回
Efficient slope reliability analysis using adaptive classification-based sampling method
.png){kind=icon}
PRE
AI
DOI：10.1007/s10064-021-02476-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Slope reliability analysis can effectively account for uncertainties involved in a slope system. However, commonly used slope reliability analysis methods often require huge computational cost, especially in large-scale problems, which hinders its wide application to engineering practice. This paper proposes an efficient slope reliability analysis method based on the active learning support vector machine (SVM) and Monte Carlo simulation (MCS). The proposed method makes use of an active learning function and cross-validation techniques to select the most suitable training samples to update the SVM model. The selected training samples are associated with a small distance to the limit state surface of the slope stability model and a large local uncertainty, which are more informative to gradually tune the SVM model to approximate the actual slope performance function. As a result, the proposed method can estimate the slope reliability with a small number of evaluations of the slope performance function, thus improving the efficiency significantly. Four slope examples are employed to demonstrate the effectiveness of the proposed method. The presented approach is also compared with some other commonly used surrogate models in slope reliability analysis. It is shown that the proposed method performs better in terms of computational efficiency to obtain similar estimation accuracy of the failure probability for the investigated examples.
Keyword:
Support vector machine
Active learning function
Response surface method
Slope reliability analysis
Monte Carlo simulation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
5.2K
被引数:
1.6W
机构
引用论文
Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes
PREAI
分享收藏A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis一种新的自适应顺序抽样方法来构造代理模型以进行有效的可靠性分析
PREAI
分享收藏
An active learning reliability method with multiple kernel functions based on radial basis function
PREAI
分享收藏Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information.png)
GEOSCIENCE FRONTIERS
OAAI
GEOSCIENCE FRONTIERSIF8.9
OAAI分享收藏
查看更多内容