上海交通大学学报(英文版) ›› 2014, Vol. 19 ›› Issue (3): 274-278.doi: 0.1007/s12204-014-1499-1

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Analysis of Epimetamorphic Rock Slopes Using Soft Computing

KUMAR Manoj1, SAMUI Pijush2*   

  1. (1. Scientist-I, National Institute of Rock Mechanics, Kolar Gold Fields 563 117, Karnataka, India; 2. Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore 632014, India)
  • 出版日期:2014-06-30 发布日期:2014-07-15
  • 通讯作者: SAMUI Pijush E-mail:pijush.phd@gmail.com

Analysis of Epimetamorphic Rock Slopes Using Soft Computing

KUMAR Manoj1, SAMUI Pijush2*   

  1. (1. Scientist-I, National Institute of Rock Mechanics, Kolar Gold Fields 563 117, Karnataka, India; 2. Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore 632014, India)
  • Online:2014-06-30 Published:2014-07-15
  • Contact: SAMUI Pijush E-mail:pijush.phd@gmail.com

摘要:

This article adopts three soft computing techniques including support vector machine (SVM), least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.

关键词: epimetemorphic rock slope, probability, support vector machine (SVM), least square support vector machine (LSSVM), relevance vector machine (RVM)

Abstract:

This article adopts three soft computing techniques including support vector machine (SVM), least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.

Key words: epimetemorphic rock slope, probability, support vector machine (SVM), least square support vector machine (LSSVM), relevance vector machine (RVM)

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