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水电岩质边坡稳定性预测的支持向量机方法
Alternative TitleSupport vector machine method for stability prediction of rock slopes in hydropower engineering regions
李秀珍1,2; 孔纪名1,2; 谢建勋3
Corresponding Author李秀珍
2011
Source Publication煤炭学报
ISSN0253-9993
Volume36Issue:S2Pages:259-263
Other Abstract

以我国水电工程区典型岩质边坡为例,选择边坡岩体质量系数、结构面方位系数、结构面类型修正系数、坡高系数及施工方法修正系数6个边坡稳定性复合指标作为评价因子,建立了水电岩质边坡稳定性预测的支持向量机方法,并将其与传统统计判别方法(如距离判别方法和Bayes判别方法)进行了比较。分析结果表明,支持向量机方法的预测精度较高,且高于传统数理统计判别方法的预测精度。 

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Took 24 typical rock slopes in hydropower engineering regions in China as examples, to build support vector machine(SVM) model for slope stability predictionbased on the classification theory of SVM. The 5 combined indexes, i. e, slope rock mass quality coefficient, orientation coefficient of structural plane, modified coefficient of structural plane types, slope height coefficient and modified coefficient of construction methods, were used as the prediction factors. The analysis results show that the prediction accuracy of the SVM method is more higher than the traditional statistical method, such as distance discriminate method and Bayes discriminate method.

Keyword水电岩质边坡 稳定性预测 支持向量机
Subject AreaTv221.2
DOI10.13225/j.cnki.jccs.2011.s2.018
Indexed ByEI
Language中文
Funding Organization国家自然科学基金资助项目(40802072) ; 云南省交通厅2010年科技教育专项资金资助项目(2010(A)08-b) ; 中国科学院“西部之光”人才培养计划资助项目(O8R2140140)
Accession numberAccession number:20114614515277
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/17838
Collection山地灾害与地表过程重点实验室
Affiliation1.中国科学院山地灾害与表生过程重点实验室
2.中国科学院水利部成都山地灾害与环境研究所
3.呼和浩特市建筑勘察设计研究院有限责任公司
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
李秀珍,孔纪名,谢建勋. 水电岩质边坡稳定性预测的支持向量机方法[J]. 煤炭学报,2011,36(S2):259-263.
APA 李秀珍,孔纪名,&谢建勋.(2011).水电岩质边坡稳定性预测的支持向量机方法.煤炭学报,36(S2),259-263.
MLA 李秀珍,et al."水电岩质边坡稳定性预测的支持向量机方法".煤炭学报 36.S2(2011):259-263.
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