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Application of a support vector machine for prediction of slope stability

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Abstract

Slope stability estimation is an engineering problem that involves several parameters. To address these problems, a hybrid model based on the combination of support vector machine (SVM) and particle swarm optimization (PSO) is proposed in this study to improve the forecasting performance. PSO was employed in selecting the appropriate SVM parameters to enhance the forecasting accuracy. Several important parameters, including the magnitude of unit weight, cohesion, angle of internal friction, slope angle, height, pore water pressure coefficient, were used as the input parameters, while the status of slope was the output parameter. The results show that the PSO-SVM is a powerful computational tool that can be used to predict the slope stability.

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References

  1. Sakellariou M G, Ferentinou M D. A study of slope stability prediction using neural networks. Geotech Geol Eng, 2005, 23: 419–445

    Article  Google Scholar 

  2. Zhang W, Chen J P, Zhang W, et al. Determination of critical slip surface of fractured rock slopes based on fracture orientation data. Sci China Tech Sci, 2013, 56: 1248–1256

    Article  Google Scholar 

  3. Liang S X, Ren X D, Li J. A random medium model for simulation of concrete failure. Sci China Tech Sci, 2013, 56: 1273–1281

    Article  Google Scholar 

  4. Liu Y R, He Z, Li B, et al. Slope stability analysis based on a multigrid method using a nonlinear 3D finite element model. Front Struct Civ Eng, 2013, 7: 24–31

    Article  Google Scholar 

  5. Duncan J M. Factors of safety and reliability in geotechnical engineering. J Geotech Geoenviron Eng, 2000, 126: 307–316

    Article  Google Scholar 

  6. Zhang Q, Wang Z Q, Xia X Z. Interface stress element method and its application in analysis of anti-sliding stability of gravity dam. Sci China Tech Sci, 2012, 55: 3285–3291

    Article  Google Scholar 

  7. Sun J P, Li J C, Liu Q Q. Search for critical slip surface in slope stability analysis by spline-based GA method. J Geotech Geoenviron Eng, 2008, 134: 252–256

    Article  Google Scholar 

  8. Li D Q, Tang X S, Zhou C B, et al. Uncertainty analysis of correlated non-normal geotechnical parameters using Gaussian copula. Sci China Tech Sci, 2012, 55: 3081–3089

    Article  Google Scholar 

  9. Han G F, Liu X L, Wang E Z. Experimental study on formation mechanism of compaction bands in weathered rocks with high porosity. Sci China Tech Sci, 2013, 56: 2563–2571

    Article  Google Scholar 

  10. Gao H M, Chen Y M, Liu H L, et al. Creep behavior of EPS composite soil. Sci China Tech Sci, 2012, 55: 3070–3080

    Article  Google Scholar 

  11. Li M, Zhang H Y. Hydrophobicity and carbonation treatment of earthern monuments in humid weather condition. Sci China Tech Sci, 2012, 55: 2313–2320

    Article  Google Scholar 

  12. Rong G, Huang K, Zhou C B, et al. A new constitutive law for the nonlinear normal deformation of rock joints under normal load. Sci China Tech Sci, 2012, 55: 555–567

    Article  Google Scholar 

  13. Jiang G L, Magnan J P. Stability analysis of embankments: Comparison of limit analysis with methods of slices. Geotechnique, 1997, 47: 857–872

    Article  Google Scholar 

  14. Dawson E M, Roth W H, Drescher A. Slope stability analysis by strength reduction. Geotechnique, 1999, 49: 835–840

    Article  Google Scholar 

  15. Cho S E. Probabilistic stability analyses of slopes using the ANN-based response surface. Comput Geotech, 2009, 36: 787–797

    Article  Google Scholar 

  16. Lin H M, Chang S K, Wu J H, et al. Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation. Eng Geol, 2009, 104: 280–289

    Article  Google Scholar 

  17. Park D, Rilett L R. Forecasting freeway link ravel times with a multi-layer feed forward neural network. Comput-Aided Civ Inf, 1999, 14: 358–367

    Google Scholar 

  18. Tang Y H, Zhang B D, Wu J J, et al. Parallel architecture and optimization for discrete-event simulation of spike neural networks. Sci China Tech Sci, 2013, 56: 509–517

    Article  Google Scholar 

  19. Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995. 1–188

    Book  MATH  Google Scholar 

  20. Osowski S, Garanty K. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng Appl Artif Intel, 2007, 20: 745–755

    Article  Google Scholar 

  21. Shin K S, Lee T S, Kim H J. An application of support vector machines in bankruptcy prediction model. Expert Syst Appl, 2005, 28: 127–135

    Article  Google Scholar 

  22. Lee Y, Lee C. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics, 2003, 19: 1132–1139

    Article  Google Scholar 

  23. Maalouf M, Khoury N, Trafalis T B. Support vector regression to predict asphalt mix performance. Int J Numer Anal Meth Geomech, 2008, 30: 983–996

    Google Scholar 

  24. Dibike Y B, Velickov S, Solomatine D P, et al. Model induction with support vector machines: introduction and applications. J Comput Civil Eng, 2001, 15: 208–216

    Article  Google Scholar 

  25. Samui P, Dixon B. Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrol Process, 2012, 26: 1361–1369

    Article  Google Scholar 

  26. Lee C Y, Chern S G. Application of a support vector machine for liquefaction assessment. J Mar Sci Tech, 2013, 21: 318–324

    Google Scholar 

  27. Ghosh S, Das S, Kundu D, et al. An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization. Neural Comput Appl, 2012, 21: 237–250

    Article  Google Scholar 

  28. Banerjee T, Das S. Multi-sensor data fusion using support vector machine for motor fault detection. Inf Sci, 2012, 217: 96–107

    Article  Google Scholar 

  29. Nasir M, Das S, Maity D, et al. A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf Sci, 2012, 209: 16–36

    Article  MathSciNet  Google Scholar 

  30. Xu H B, Chen G H. An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mech Syst Signal Pr, 2013, 35:167–175

    Article  Google Scholar 

  31. Yilmaz A E, Kuzuoglu M. A particle swarm optimization approach for hexahedral mesh smoothing. Int J Numer Meth Fluids, 2009, 60: 55–78

    Article  MATH  Google Scholar 

  32. Afshar M H, Rajabpour R. Application of local and global particle swarm optimization algorithms to optimal design and operation of irrigation pumping systems. Irrig Drain, 2009, 58: 321–331

    Article  Google Scholar 

  33. Espinoza M, Suykens J A K, Moor B De. Fixed-size least squares support vector machines: a large scale application in electrical load forecasting. Comput Manag Sci, 2006, 3: 113–129

    Article  MathSciNet  MATH  Google Scholar 

  34. Kennedy J, Eberhart R C. Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks 4, Perth, Australia. IEEE Service Center: Piscataway, NJ,1995. 1942–1948

    Google Scholar 

  35. Parsopoulos K E, Vrahatis M N. Recent approaches to global optimization problems through particle swarm optimization. Natural Computing, 2002, 1: 235–306

    Article  MathSciNet  MATH  Google Scholar 

  36. Pardo M, Sberveglieri G. Classification of electronic nose data with support vector machines. Sensor Actuat B-Chem, 2005, 107: 730–737

    Article  Google Scholar 

  37. Trelea I C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inform Process Lett, 2003, 85: 317–325

    Article  MathSciNet  MATH  Google Scholar 

  38. Mahesh P. Support vector machines-based modeling of seismic liquefaction potential. Int J Numer Anal Meth Geomech, 2006, 30: 983–996

    Article  MATH  Google Scholar 

  39. Mohammadnejad M, Gholami R, Ramezanzadeh A, et al. Prediction of blast-induced vibrations in limestone quarries using Support Vector Machine. J Vib Control, 2011, 18: 1322–1329

    Article  Google Scholar 

  40. Khandelwal M, Singh T N. Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng, 2007, 27: 116–125

    Article  Google Scholar 

  41. Chakravarty S, Dash P K. A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices. Appl Soft Comput, 2012, 12: 931–941

    Article  Google Scholar 

  42. Khandelwal M. Blast-induced ground vibration prediction using support vector machine. Eng Comput, 2011, 27: 193–200

    Article  Google Scholar 

  43. Khandelwal M. Evaluation and prediction of blast induced ground vibration using support vector machine. Int J Rock Mech Min Sci, 2010, 47: 509–516

    Article  Google Scholar 

  44. Liu K Y, Qiao C S, Tian S F. Design of tunnel shotcrete-bolting support based on a support vector machine approach. Int J Rock Mech Min Sci, 2004, 41: 510–511

    Article  Google Scholar 

  45. Khandelwal M, Kankar P K, Harsha S P. Evaluation and prediction of blast induced ground vibration using support vector machine. Min Sci Tech, 2010, 20: 64–70

    Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, China

    XinHua Xue & XingGuo Yang

  2. College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China

    XinHua Xue & Xin Chen

Authors
  1. XinHua Xue
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  2. XingGuo Yang
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  3. Xin Chen
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Correspondence to XinHua Xue or XingGuo Yang.

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Xue, X., Yang, X. & Chen, X. Application of a support vector machine for prediction of slope stability. Sci. China Technol. Sci. 57, 2379–2386 (2014). https://doi.org/10.1007/s11431-014-5699-6

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  • DOI: https://doi.org/10.1007/s11431-014-5699-6

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