A BBO-based algorithm for slope stability analysis by locating critical failure surface

  • Jayraj Singh
  • Haider Banka
  • Amit Kumar Verma
Original Article


Determination of the critical failure surface is performed in stability evaluation process for road cut slope, embankments, dam, excavations, retaining walls and many more. Finding the critical failure surface in a rock or soil slope is very cumbersome and becomes a difficult constrained global optimization problem. Due to existence of discontinuous function and strong multiple local minima points, researchers are facing difficulties to employ trial-and-error methods in a large search space. Thus, classical optimization techniques fail to converge to a valid solution. In this study a stochastic method called biogeography-based optimization algorithm was adopted for analyzing the factor of safety. Based on the finding from the implementation and quantitative evaluation, it was found that the proposed method for locating critical failure surface in homogeneous soil slope acquires more efficient results over other implemented methods such as grid search and genetic algorithm. The validation and effectiveness of the proposed method are investigated by solving two benchmark case studies from the literature, while the simulation design for slip surfaces is carried out using ‘Rocscience slide’ software tool for comparing the results.


Slope stability analysis Critical failure surface Factor of safety BBO algorithm 


Compliance with ethical standards

Conflict of interest

We hereby declare that we are having no conflict of interest


  1. 1.
    Vageesha S, Mathada G, Venkatachalam G, Srividya A (2007) Slope stability assessment-a comparison of probabilistic, possibilistic and hybrid approaches. Int J Perform Eng 3(2):231–242Google Scholar
  2. 2.
    Dodagoudar GR, Venkatachalam G (2000) Reliability analysis of slopes using fuzzy sets theory. Comput Geotech 27(2):101–115CrossRefGoogle Scholar
  3. 3.
    Rubio E, Hall JW (2004) Uncertainty analysis in a slope hydrology and stability model using probabilistic and imprecise information. Comput Geotech 31(7):529–536CrossRefGoogle Scholar
  4. 4.
    Zhang Z, Liu Z, Zheng L, Zhang Y (2014) Development of an adaptive relevance vector machine approach for slope stability inference. Neural Comput Appl 25(7–8):2025–2035CrossRefGoogle Scholar
  5. 5.
    Aryal KP (2006) Slope stability evaluations by limit equilibrium and finite element methods. Ph.D. thesis, Norwegian University of Science and TechnologyGoogle Scholar
  6. 6.
    Fellenius W (1936) Calculation of the stability of earth dams. In: Transactions of the 2nd congress on large dams, Washington, DC, vol 4, pp 445–463. International Commission on Large Dams (ICOLD), ParisGoogle Scholar
  7. 7.
    Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Geotechnique 5(1):7–17CrossRefGoogle Scholar
  8. 8.
    Morgenstern NR, Eo V, Eo Price V (1965) The analysis of the stability of general slip surfaces. Geotechnique 15(1):79–93CrossRefGoogle Scholar
  9. 9.
    Spencer E (1967) A method of analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique 17(1):11–26CrossRefGoogle Scholar
  10. 10.
    Janbu N (1975) Slope stability computations: In: Hirschfeld RC, Poulos SJ (eds) Embankment-dam engineering. Wiley, New York, 1973. Int J Rock Mech Min Sci Geomech Abstr 12: 67 (Pergamon)Google Scholar
  11. 11.
    Abramson LW (2002) Slope stability and stabilization methods. Wiley, New YorkGoogle Scholar
  12. 12.
    Ching RKH, Fredlund DG (1983) Some difficulties associated with the limit equilibrium method of slices. Canad Geotech J 20(4):661–672CrossRefGoogle Scholar
  13. 13.
    Baker R, Garber M (1978) Theoretical analysis of the stability of slopes. Geotechnique 28(4):395–411CrossRefGoogle Scholar
  14. 14.
    Greco VR (1996) Efficient monte carlo technique for locating critical slip surface. J Geotech Eng 122(7):517–525CrossRefGoogle Scholar
  15. 15.
    Yamagami T, Ueta Y (1986) Noncircular slip surface analysis of the stability of slopes. Landslides 22(4):8–16CrossRefGoogle Scholar
  16. 16.
    Kaswan A, Singh V, Jana PK (2018) A novel multi-objective particle swarm optimization based energy efficient path design for mobile sink in wireless sensor networks. Pervasive Mobile Comput. Google Scholar
  17. 17.
    Kaswan A, Tomar A, Jana PK (2018) A GSA-based scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks. J Network Comp Appl. Google Scholar
  18. 18.
    Singh J, Verma AK, Banka H et al (2016) A study of soft computing models for prediction of longitudinal wave velocity. Arab J Geosci 9:224CrossRefGoogle Scholar
  19. 19.
    Das SK, Tripathi S (2017) Adaptive and intelligent energy efficient routing for transparent heterogeneous ad-hoc network by fusion of game theory and linear programming. Appl Intell. Google Scholar
  20. 20.
    Chen Z, Morgenstern NR (1983) Extensions to the generalized method of slices for stability analysis. Canad Geotech J 20(1):104–119CrossRefGoogle Scholar
  21. 21.
    McCombie P, Wilkinson P (2002) The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis. Comput Geotech 29(8):699–714CrossRefGoogle Scholar
  22. 22.
    Sarat Kumar Das (2005) Slope stability analysis using genetic algorithm. Electron J Geotech Eng 10:429–439Google Scholar
  23. 23.
    Zolfaghari AR, Heath AC, McCombie PF (2005) Simple genetic algorithm search for critical non-circular failure surface in slope stability analysis. Comput Geotech 32(3):139–152CrossRefGoogle Scholar
  24. 24.
    Sun J, Li J, Liu Q (2008) Search for critical slip surface in slope stability analysis by spline-based ga method. J Geotech Geoenviron Eng 134(2):252–256CrossRefGoogle Scholar
  25. 25.
    Sengupta A, Upadhyay A (2009) Locating the critical failure surface in a slope stability analysis by genetic algorithm. Appl Soft Comp 9(1):387–392CrossRefGoogle Scholar
  26. 26.
    Kahatadeniya KS, Nanakorn P, Neaupane KM (2009) Determination of the critical failure surface for slope stability analysis using ant colony optimization. Eng Geol 108(1):133–141CrossRefGoogle Scholar
  27. 27.
    Ahangar-Asr A, Faramarzi A, Javadi AA (2010) A new approach for prediction of the stability of soil and rock slopes. Eng Comput 27(7):878–893CrossRefzbMATHGoogle Scholar
  28. 28.
    Khajehzadeh M, Taha MR, El-Shafie A, Mohammad K (2011) Search for critical failure surface in slope stability analysis by gravitational search algorithm. Int J Phys Sci 6(21):5012–5021Google Scholar
  29. 29.
    Khajehzadeh M, Taha MR, El-Shafie A, Eslami M (2012) A modified gravitational search algorithm for slope stability analysis. Eng Appl Artif Intell 25(8):1589–1597CrossRefGoogle Scholar
  30. 30.
    Kashani AR, Gandomi AH, Mousavi M (2016) Imperialistic competitive algorithm: a metaheuristic algorithm for locating the critical slip surface in 2-dimensional soil slopes. Geosci Front 7(1):83–89Google Scholar
  31. 31.
    Fredlund DG, Krahn J (1977) Comparison of slope stability methods of analysis. Canad Geotech J 14(3):429–439CrossRefGoogle Scholar
  32. 32.
    Cheng YM, Lau CK (2014) Slope stability analysis and stabilization: new methods and insight. CRC Press, Boca RatonCrossRefGoogle Scholar
  33. 33.
    Huang YH (2014) Slope stability analysis by the limit equilibrium method: fundamentals and methods. American Society of Civil Engineers, RestonCrossRefGoogle Scholar
  34. 34.
    Krahn J (2003) The 2001 rm hardy lecture: the limits of limit equilibrium analyses. Canad Geotech J 40(3):643–660CrossRefGoogle Scholar
  35. 35.
    Wu A (2012) Locating general failure surfaces in slope analysis via cuckoo search. Accessed 27 Jan 2017
  36. 36.
    Cheng YM, Li L, Chi SC (2007) Performance studies on six heuristic global optimization methods in the location of critical slip surface. Comput Geotech 34(6):462–484CrossRefGoogle Scholar
  37. 37.
    Chen Z-Y, Shao C-M (1988) Evaluation of minimum factor of safety in slope stability analysis. Canad Geotech J 25(4):735–748CrossRefGoogle Scholar
  38. 38.
    Nguyen VU (1985) Determination of critical slope failure surfaces. J Geotech Eng 111(2):238–250CrossRefGoogle Scholar
  39. 39.
    Cheng YM (2003) Location of critical failure surface and some further studies on slope stability analysis. Comput Geotech 30(3):255–267CrossRefGoogle Scholar
  40. 40.
    Abdallah I, Husein Malkawi AI, Hassan WF, Sarma SK (2001) Global search method for locating general slip surface using monte carlo techniques. J Geotech Geoenviron Eng 127(8):688–698CrossRefGoogle Scholar
  41. 41.
    Jade S, Shanker KD (1995) Modelling of slope failure using a global optimization technique. Eng Optim+ A35 23(4):255–266CrossRefGoogle Scholar
  42. 42.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRefGoogle Scholar
  43. 43.
    Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Feng C, Wang Q (2016) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl 75(23):15601–15617CrossRefGoogle Scholar
  44. 44.
    Wang S, Zhang Y, Ji G, Yang J, Jianguo W, Wei L (2015) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic abc and biogeography-based optimization. Entropy 17(8):5711–5728CrossRefGoogle Scholar
  45. 45.
    Yadav RK, Banka H (2016) Ibbomsa: an improved biogeography-based approach for multiple sequence alignment. Evol Bioinform 12:237CrossRefGoogle Scholar
  46. 46.
    Song Y, Liu M, Wang Z (2010) Biogeography-based optimization for the traveling salesman problems. In: 2010 Third international joint conference on computational science and optimization (CSO), vol 1, pp 295–299. IEEEGoogle Scholar
  47. 47.
    MacArthur R, Wilson E (1967) The theory of biogeography. Princeton University Press, PrincetonGoogle Scholar
  48. 48.
    Guo W, Chen M, Wang L, Mao Y, Wu Q (2017) A survey of biogeography-based optimization. Neural Comput Appl 28(8):1909–1926CrossRefGoogle Scholar
  49. 49.
    Das SK, Tripathi S (2017) Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques. Int J Commun Syst 30(16)1–16. CrossRefGoogle Scholar
  50. 50.
    Lalwani P, Banka H, Kumar C (2016) BERA: a biogeography-based energy saving routing architecture for wireless sensor networks. Soft Comput 22(5):1651–1667Google Scholar
  51. 51.
  52. 52.
    Kostic S, Vasovic N, Sunaric D (2015) A new approach to grid search method in slope stability analysis using box-behnken statistical design. Appl Math Comput 256:425–437MathSciNetzbMATHGoogle Scholar
  53. 53.
    Solati S, Habibagahi G (2006) A genetic approach for determining the generalized interslice forces and the critical non-circular slip surface. Iran J Sci Technol Trans B Eng 30(1):1–20Google Scholar
  54. 54.
    Baker R (1980) Determination of the critical slip surface in slope stability computations. Int J Numer Anal Methods Geomech 4(4):333–359CrossRefzbMATHGoogle Scholar
  55. 55.
    Rocscience slide. Accessed 10 Dec 2016

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringIndian Institute of Technology (ISM)DhanbadIndia
  2. 2.Department of Mining EngineeringIndian Institute of Technology (ISM)DhanbadIndia

Personalised recommendations