.png){kind=icon}
返回
Relevance vector machine applied to slope stability analysis
.png){kind=icon}
PRE
AI
DOI：10.1002/nag.1037.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper examines the potential of relevance vector machine (RVM) in slope stability analysis. The nonlinear relationship between slope stability and its influence factors is presented by the relevance vector learning mechanism based on a kernel-based Bayesian framework. The six input variables used for the RVM for the prediction of stability slope are density (?), friction angle (C), friction coefficient (?), slope angle (?r), slope height (H), and pore water pressure (ru). Comparison of RVM with some other methods is also presented. RVM has been used to compute the error bar. The results presented in this paper clearly highlight that the RVM is a robust tool for the prediction of slope stability. The experimental results show the effectiveness of the proposed approach. Copyright (c) 2011 John Wiley & Sons, Ltd.
Keyword:
slope engineering
stability analysis
relevance vector machine
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:
3.6
论文数:
3.3K
被引数:
9.6K
机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏
Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China.png)
GEOMORPHOLOGY
GEOMORPHOLOGYIF3.3
PREAI
分享收藏Postural Asymmetry and Movement Disorder After Unilateral Microinjection of Adrenocorticotropin 1-24 in Rat BrainstemScience
ScienceIF0
PREAI
分享收藏
查看更多内容