基于HIOA-MK-TCRVM算法的边坡稳定性估计模型

doi:10.16265/j.cnki.issn1003-3033.2017.01.021

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (1): 116-121.doi: 10.16265/j.cnki.issn1003-3033.2017.01.021

基于HIOA-MK-TCRVM算法的边坡稳定性估计模型

罗亦泳^1,2 讲师, 姚宜斌² 教授, 张立亭¹ 教授, 周世健¹ 教授

1 东华理工大学测绘工程学院,江西南昌 330013
2 武汉大学测绘学院,湖北武汉 430079

收稿日期:2016-11-04 修回日期:2016-12-28
作者简介:罗亦泳 (1982—),男,江西南昌人,博士,讲师,主要从事建筑物安全监测数据智能分析理论与方法研究。E-mail:luoyiyong@whu.edu.cn。
基金资助:
国家自然科学基金资助(41374007);江西省自然科学基金资助(20151BAB213031);江西省教育厅科学技术研究项目(GJJ150592)。

Slope stability estimation model based on HIOA-MK-TCRVM

LUO Yiyong^1,2, YAO Yibin², ZHANG Liting¹, ZHOU Shijian¹

1 School of Geomatics, East China University of Technology, Nanchang Jiangxi 330013, China
2 School of Geodesy and Geomatics, Wuhan University, Wuhan Hubei 430079, China

Received:2016-11-04 Revised:2016-12-28

摘要/Abstract

摘要： 为提高边坡稳定性估计方法的精度及计算效率,将混合智能优化算法(HIOA)与多核二分类相关向量机(MK-TCRVM)算法相结合,建立HIOA优化的MK-TCRVM(HIOA-MK-TCRVM)算法,并用其估计岩质边坡及土质边坡稳定性。同时,基于单核二分类相关向量机、支持向量机(SVM)等算法建立其他的边坡稳定性估计模型,并与HIOA-MK-TCRVM算法进行精度与稀疏性对比分析。最后,分析HIOA算法优化MK-TCRVM算法参数的效果。结果表明,HIOA-MK-TCRVM算法对训练集与测试集边坡稳定性估计的准确率均达到100%,其精度优于其他边坡稳定性估计模型;HIOA-MK-TCRVM算法的相关向量数占训练样本数的25%以内,模型稀疏化效果明显;向HIOA算法中加入遗传操作后,其进化速度及最优解均得到较好的改善。

Abstract: In order to improve the accuracy and computational efficiency of slope stability estimation, an MK-TCRVM based on HIOA (HIOA-MK-TCRVM)was constructed. Then the HIOA-MK-TCRVM was applied to the field of rock slope and soil slope stability estimation. At the same time, other slope stability estimation models (they were based on single kernel two classification relevance vector machine, and support vector machine (SVM) among other algorithms) were established, and a comparison was made between the models and the HIOA-MK-TCRVM for the accuracy and the sparsity. Finally, the effect of optimizing MK-TCRVM parameters based on HIOA was analyzed. The experimental results show that the accuracy rates of HIOA-MK-TCRVM for the training set and the test set are 100%, which are better than those of the other methods. The proportion of the correlation vector of HIOA-MK-TCRVM to the number of training samples is less than 25%,which confirms that HIOA-MK-TCRVM has an obvious sparse effect. After the genetic operation is added to the HIOA algorithm, the evolution speed and the optimal solution of HIOA are improved.

Key words: multi-kernel two-class relevance vector machine(MK-TCRVM) , slope stability estimation, hybrid intelligent optimization algorithm (HIOA), particle swarm optimization(PSO), two-class relevance vector machine(TCRVM)

中图分类号:

X913.4

罗亦泳, 姚宜斌, 张立亭, 周世健. 基于HIOA-MK-TCRVM算法的边坡稳定性估计模型[J]. 中国安全科学学报, 2017, 27(1): 116-121.

LUO Yiyong, YAO Yibin, ZHANG Liting, ZHOU Shijian. Slope stability estimation model based on HIOA-MK-TCRVM[J]. China Safety Science Journal, 2017, 27(1): 116-121.

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基于HIOA-MK-TCRVM算法的边坡稳定性估计模型

Slope stability estimation model based on HIOA-MK-TCRVM

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