Geomechanics and Engineering

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Sensitivity analysis of the influencing factors of slope stability based on LS-SVM

  • Xu, Juncai (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Ren, Qingwen (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Shen, Zhenzhong (College of Water Conservancy and Hydropower Engineering, Hohai University)
  • Received : 2016.02.13
  • Accepted : 2017.03.27
  • Published : 2017.09.25
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Abstract

This study proposes a sensitivity analysis method for slope stability based on the least squares support vector machine (LS-SVM) to examine the influencing factors of slope stability. The method uses LS-SVM as an algorithm for machine learning. An appropriate training dataset is established according to the slope characteristics, and a testing dataset is designed orthogonally. Results of the testing data in the experiment design are calculated after training using the LS-SVM model. The sensitivity of the slope stability of each factor is examined via gray correlation analysis. The results are consistent with those of the traditional Bishop analysis and can be used as a reference for optimizing slope design.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  1. Berisavljevic, Z., Berisavljevic, D., Cebasek, V. and Rakic, D. (2015), "Slope stability analyses using limit equilibrium and strength reduction methods", Gradevinar, 67(10), 975-983.
  2. Chen, G.F., Chen, S.G. and Lu, Y.F. (2007), "Sensitivity analysis of slope stability based on uniform design", J. Hydraulic Eng., 38(11), 1397-1401.
  3. Chen, C., Xiao, Z. and Zhang, G. (2011), "Stability assessment model for epimetamorphic rock slopes based on adaptive neuro-fuzzy inference system", Electron. J. Geotech. Eng., 16(A), 93-107.
  4. Chen, J., Tan, L., Zhang, Q., Yang, Y., Yang, Z., Zhang, Y., Zhu, Z., Chen, K. and Xu, L. (2015), "Grey correlation analysis of Artemisinin content in Artemisiae annie in Three Gorges Reservoir Region", Medicinal Plant, 6(1/2), 28.
  5. De Vita, P., Napolitano, E., Godt, J.W. and Baum, R.L. (2013), "Deterministic estimation of hydrological thresholds for shallow landslide initiation and slope stability models: Case study from the Somma-Vesuvius area of southern Italy", Landslides, 10(6), 713-728. https://doi.org/10.1007/s10346-012-0348-2
  6. Dehnavi, A., Aghdam, I.N., Pradhan, B. and Varzandeh, M.H.M. (2015), "A new hybrid model using stepwise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran", Catena, 135, 122-148. https://doi.org/10.1016/j.catena.2015.07.020
  7. Eid, H.T. and Rabie, K.H. (2016), "Fully softened shear strength for soil slope stability analyses", Int. J. Geomech., 17(1), 04016023.
  8. Farah, K., Ltifi, M. and Hassis, H. (2015), "A study of probabilistic FEMs for a slope reliability analysis using the stress fields", Open Civil Eng. J., 9(1), 196-206. https://doi.org/10.2174/1874149501509010196
  9. Gao, Y.F., Zhang, F., Lei, G. and Li, D.Y. (2013), "An extended limit analysis of three-dimensional slope stability", Geotechnique, 63(6), 518. https://doi.org/10.1680/geot.12.T.004
  10. Garg, A., Garg, A., Tai, K. and Sreedeep, S. (2014), "Estimation of factor of safety of rooted slope using an evolutionary approach", Ecol. Eng., 64, 314-324. https://doi.org/10.1016/j.ecoleng.2013.12.047
  11. Ge, S.S., Hang, C.C., Lee, T.H. and Zhang, T. (2013), Stable Adaptive Neural Network Control, Volume 13, Springer Science & Business Media.
  12. He, X., Li, S.J., Liu, Y.X. and Zhou, Y.P. (2003), "Analyzing method of rock slope stability based on artificial neural network", Rock Soil Mech., S2, 73-76.
  13. Li, W., Liu, H., Yang, P. and Xie, W. (2015), "Supporting regularized logistic regression privately and efficiently", Plos One, 11(6), e156479.
  14. Liu, J.N. and Hu, Y. (2013), "Application of feature-weighted support vector regression using grey correlation degree to stock price forecasting", Neural Comput. Appl., 22(1), 143-152. https://doi.org/10.1007/s00521-011-0677-4
  15. Mohamed, T., Kasa, A. and Mukhlisin, M. (2012), "Prediction of slope stability using statistical method and fuzzy logic", The Online J. Sci. Technol., 2(4), 68-73.
  16. Moraes, R., Valiati, J.F. and Neto, W.P.G. (2013), "Document-level sentiment classification: An empirical comparison between SVM and ANN", Expert Syst. Appl., 40(2), 621-633. https://doi.org/10.1016/j.eswa.2012.07.059
  17. Myhra, K.S., Etzelmuller, B. and Blikra, L.H. (2014), "Sensitivity of rock slope stability in relation to thermal regime - examples from norway", J. Biol. Chem., 289(3), 1540-1550. https://doi.org/10.1074/jbc.M113.498246
  18. Ozbay, A. and Cabalar, A.F. (2014), "FEM and LEM stability analyses of the fatal landslides at Collolar open-cast lignite mine in Elbistan, Turkey", Landslides, 12(1), 155-163. https://doi.org/10.1007/s10346-014-0537-2
  19. Qi, Z.F., Jiang, Q.H., Zhou, C.B., Xiang, B.Y. and Shao, J.D. (2013), "A new slope displacement back analysis method based on v-SVR and MVPSO algorithm and its application", Chinese J. Rock Mech. Eng., 32(6), 1185-1196.
  20. Sakellariou, M.G. and Ferentinou, M.D. (2005), "A study of slope stability prediction using neural networks", Geotech. Geol. Eng., 23(4), 419-445. https://doi.org/10.1007/s10706-004-8680-5
  21. Sdao, F., Lioi, D.S., Pascale, S., Caniani, D. and Mancini, I.M. (2013), "Landslide susceptibility assessment by using a neuro-fuzzy model: A case study in the Rupestrian heritage rich area of Matera", Natural Hazard. Earth Syst. Sci., 13(2), 395-407. https://doi.org/10.5194/nhess-13-395-2013
  22. Singh, R., Kainthola, A. and Singh, T.N. (2012), "Estimation of elastic constant of rocks using an ANFIS approach", Appl. Soft Comput., 12(1), 40-45. https://doi.org/10.1016/j.asoc.2011.09.010
  23. Vatanpour, N., Ghafoori, M. and Talouki, H.H. (2014), "Probabilistic and sensitivity analyses of effective geotechnical parameters on rock slope stability: A case study of an urban area in northeast Iran", Natural Hazard., 71(3), 1659-1678. https://doi.org/10.1007/s11069-013-0982-6
  24. Yu, H.C., Liu, H., Yu, H. and Liu, P.H. (2008), "Application of rough set to sensitivity analysis of influencing factors for slope stability based on FCM algorithm", Rock Soil Mech., 29(7), 1889-1894.
  25. Zhang, Z., Liu, Z., Zheng, L. and Zhang, Y. (2014), "Development of an adaptive relevance vector machine approach for slope stability inference", Neural Comput. Appl., 25(7-8), 2025-2035. https://doi.org/10.1007/s00521-014-1690-1

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