Skip to main content

Advertisement

SpringerLink
Log in

Slope stability analysis using artificial intelligence techniques

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Natural and man-made soil slope failures are complex phenomena and cause serious hazards in many countries of the world. As a result, public and private property is damaged worth millions of dollars. It is necessary to understand the processes causing failure of slopes and prediction of its vulnerability for proper mitigation of slope failure hazards. Various attempts have been made to predict the stability of slope using both conventional methods such as limit equilibrium method, finite element method, finite difference method and statistical methods. Artificial intelligence (AI) methods like artificial neural networks, genetic programming and genetic algorithms, and support vector machines are found to have better efficiency compared to statistical methods. The present study is an attempt to use recently developed AI methods such as functional networks (FNs), multivariate adaptive regression splines (MARS), and multigene genetic programming (MGGP) to predict factor of safety of slope using slope stability data available in the literature. Prediction model equations are also provided, which can be used to predict the factor of safety of a slope. The performances of these AI techniques have been evaluated in terms of different statistical parameters such as average absolute error, maximum absolute error, root mean square error, correlation coefficient, and Nash–Sutcliff coefficient of efficiency. Of the available models, MARS model had better prediction performance in comparison with MGGP and FN models in terms of the above statistical criteria.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abu-Farsakh MY, Titi HH (2004) Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. J Geotech Geoenviron Eng 130(9):935–944

    Article  Google Scholar 

  • Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3):242–274

    Article  Google Scholar 

  • Attoh-Okine NO (2005) Modeling incremental pavement roughness using functional network. Can J Civ Eng 32:899–905

    Article  Google Scholar 

  • Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Geotechnique 5(1):7–17

    Article  Google Scholar 

  • Bishop AW, Morgenstern NR (1960) Stability coefficients for earth slopes. Geotechnique 10(4):129–150

    Article  Google Scholar 

  • Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45:55–72

    Article  Google Scholar 

  • Castillo E (1998) Functional networks. Neural processing letter 7:151–159

    Article  Google Scholar 

  • Castillo E, Ruiz-Cobo R (1992) Functional equations in science and engineering. Marcel Dekker, New York

    Google Scholar 

  • Castillo E, Cobo A, Gutierrez JM, Pruneda RE (1999) Working with differential, functional and difference equations using functional networks. Appl Math Model 23:89–107

    Article  Google Scholar 

  • Castillo E, Cobo A, Manuel J, Gutierrez JM, Pruneda E (2000) Functional networks: a new network-based methodology. Comput Aided Civ Infrastruct Eng 15:90–106

    Article  Google Scholar 

  • Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 33:454–459

    Article  Google Scholar 

  • Das SK, Basudhar PK (2008) Prediction of residual friction angle of clays using artificial neural network. Eng Geol 100(3–4):142–145

    Article  Google Scholar 

  • Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential neural networks. Environ Earth Sci 64:201–210

    Article  Google Scholar 

  • Das SK, Sivakugan N (2010) Discussion of Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 37(8):928–930

    Article  Google Scholar 

  • El-Sebakhy EA, Faisal KA, Helmy T, Azzedin F, Al-Suhaim A (2006) Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. In: Proceeding of the 4th ACS/IEEE international conference on computer systems and applications, 281–287

  • El-Sebakhy EA, Abdulraheem A, Raharja P, Azzedin F, Sheltami T, Ahmed M (2007) Functional network as a novel approach for prediction of permeability in a carbonate reservoir. In Proceedings SPE conference, 11–14

  • Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Environ 74(1):103–116. doi:10.1007/s10064-014-0606-8

    Article  Google Scholar 

  • Erzin Y, Turkoz D (2015) Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Comput Appl. doi:10.1007/s00521-015-1943-7

    Google Scholar 

  • Fellenius W (1936) Calculation of the stability of earth dams. In: Transactions of the 2nd international congress on large dams, Washington, USA, 445–462

  • Friedman J (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141

    Article  Google Scholar 

  • Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239

    Article  Google Scholar 

  • Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and Earthquake Engineering Problems. Neural Comput Appl 21(1):189–201

    Article  Google Scholar 

  • Giustolisi O, Doglioni A, Savic DA, Webb BW (2007) A multi-model approach to analysis of environmental phenomena. Environ Model Softw 22(5):674–682

    Article  Google Scholar 

  • Kanungo DP, Pain A, Sharma S (2013) Finite element modeling approach to assess the stability of debris and rock slopes: a case study from the Indian Himalayas. Nat Hazards 69:1–24

    Article  Google Scholar 

  • Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 41(3):1–18. doi:10.1007/s11069-014-1106-7

    Google Scholar 

  • Lu P, Rosenbaum MS (2003) Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 30(3):383–398

    Article  Google Scholar 

  • Manouchehrian A, Gholamnejad J, Sharifzadeh M (2014) Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environ Earth Sci 71:1267–1277

    Article  Google Scholar 

  • Math Work Inc. (2005) Matlab User’s Manual, Version 6.5. Natick, Massachusetts

  • Morgenstern NR, Price VE (1965) The analysis of the stability of general slip surfaces. Geotechnique 15(1):79–93

    Article  Google Scholar 

  • Muduli PK, Das SK (2013a) First order reliability method for probabilistic evaluation of liquefaction potential of soil using genetic programming. J Geomechanics, Int. doi:10.1061/(ASCE)GM.1943-5622.0000377

    Google Scholar 

  • Muduli PK, Das SK (2013b) SPT-based probabilistic method for evaluation of liquefaction potential of soil using multi-gene genetic programming. Int J Geotech Earthquake Eng 4(1):42–60

    Article  Google Scholar 

  • Muduli PK, Das PK (2014a) Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model. Acta Geophys 62(3):529–543

    Article  Google Scholar 

  • Muduli PK, Das PK (2014b) CPT-based seismic liquefaction potential evaluation using multi-gene genetic programming approach. Indian Geotech J 44(1):86–93

    Article  Google Scholar 

  • Muduli PK, Das MR, Samui P, Das SK (2013) Uplift capacity of suction caisson in clay using artificial intelligence techniques. Mar Georesour Geotechnol 31(4):375–390

    Article  Google Scholar 

  • Muduli PK, Das PK, Bhattacharya S (2014) CPT-based probabilistic evaluation of seismic soil liquefaction potential using multi-gene genetic programming. Georisk Assess Manag Risk Eng Syst Geohazards 8(1):14–28

    Article  Google Scholar 

  • Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71(1):523–547

    Article  Google Scholar 

  • Rajasekaran S (2004) Functional networks in structural engineering. J Comput Civ Eng 18:172–181

    Article  Google Scholar 

  • Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC (2013) Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Hazards 65(1):315–330

    Article  Google Scholar 

  • Sah NK, Sheorey PR, Upadhyama LW (1994) Maximum likelihood estimation of slope stability. Int J Rock Mech Min Sci Geomech Abstr 31:47–53

    Article  Google Scholar 

  • Sakellariou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23:419–445

    Article  Google Scholar 

  • Samui P (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56:255–267

    Article  Google Scholar 

  • Samui P, Kumar B (2006) Artificial neural network prediction of stability numbers for two-layered slopes with associated flow rule. The Electronic Journal of Geotechnical Engineering. http://www.ejge.com/2006/Ppr0626/Abs0626.htm. Accessed 17 July 2014

  • Samui P, Das S, Kim D (2011) Uplift capacity of suction caisson in clay using multivariate adaptive regression spline. Ocean Eng 38(17–18):2123–2127

    Article  Google Scholar 

  • Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox from multi-gene symbolic regression. In: Proceedings of the International multi conference of engineers and computer scientists, Hong Kong, vol 1(3), 77–80

  • Singh TN, Gulati A, Dontha L, Bhardwaj V (2008) Evaluating cut slope failure by numerical analysis—a case study. Nat Hazards 47(2):263–279

    Article  Google Scholar 

  • Smith GN (1986) Probability and statistics in civil engineering: an introduction. Collins, London

    Google Scholar 

  • Wang HB, Xu WY, Xu RC (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80:302–315

    Article  Google Scholar 

  • Xu W, and Xie S (1999) Slope stability analysis and evaluation with probabilistic artificial neural Network method. Site Investig Sci Technol 3. http://periodical.wanfangdata.com.cn/peridicals/kckxjs/kckx9903/990304.htm

  • Yang CX, Tham LG, Feng XT, Wang YJ, Lee PKK (2004) Two-stepped evolutionary algorithm and its application to stability analysis of slopes. J Comput Civil Eng 18(2):145–153

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India

    Shakti Suman & S. K. Das

  2. Civil Engineering Department, BIET, BPUT, Bhadrakh, Odisha, India

    S. Z. Khan

  3. Civil Engineering Department, IGIT, Sarang, Odisha, 759146, India

    S. K. Chand

Authors
  1. Shakti Suman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Z. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. K. Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. K. Chand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. K. Das.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suman, S., Khan, S.Z., Das, S.K. et al. Slope stability analysis using artificial intelligence techniques. Nat Hazards 84, 727–748 (2016). https://doi.org/10.1007/s11069-016-2454-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-016-2454-2

Keywords

Search

Navigation