Skip to main content
Log in

A study of slope stability prediction using neural networks

  • Published:
Geotechnical & Geological Engineering Aims and scope Submit manuscript


The determination of the non-linear behaviour of multivariate dynamic systems often presents a challenging and demanding problem. Slope stability estimation is an engineering problem that involves several parameters. The impact of these parameters on the stability of slopes is investigated through the use of computational tools called neural networks. A number of networks of threshold logic unit were tested, with adjustable weights. The computational method for the training process was a back-propagation learning algorithm. In this paper, the input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FS) that can be modelled as a function approximation problem, or the stability status (S) that can be modelled either as a function approximation problem or as a classification model. The performance of the network is measured and the results are compared to those obtained by means of standard analytical methods. Furthermore, the relative importance of the parameters is studied using the method of the partitioning of weights and compared to the results obtained through the use of Index Information Theory.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others


  • A.W. Bishop (1955) ArticleTitleThe use of slip circle in the stability analysis of earth slopes Geotechnique. 5 7–17

    Google Scholar 

  • H.-C Chang D.C. Kopaska-Merkel H.C. Chen (2002) ArticleTitleIdentification of lithofacies using Kohonen self-organising maps Comput. Geosci. 28 223–229 Occurrence Handle10.1016/S0098-3004(01)00067-X

    Article  Google Scholar 

  • Cilliers P. (1999). Complexity, Postmodernism, Routledge. Taylor & Francis Group

  • J.H. Deng C.F. Lee (2001) ArticleTitleDisplacement back analysis for a steep slope at the Three Gorges Project Site Int. J. Rock Mech. Min. Sci. 38 259–268 Occurrence Handle10.1016/S1365-1609(00)00077-0

    Article  Google Scholar 

  • Duncan M. Soil slope stability analysis, Landslides Investigation and Mitigation, Transportation Research Board Special Report 247, Washington Press, 1996, pp. 337-371

  • Ferentinou, M.: Investigation of Landslide Hazard estimation with Computational Neural Networks in a Geographical Information System Environment, PhD Thesis, in press.

  • Ferentinou, M., Sakellariou, M.: Slope stability estimation using GIS, In: Rosenbaum and Turner (eds.), Characterisation of the Shallow Subsurface: Implications for Urban Infrastructureand Environmental Assessment, Springer-Verlag, Duesseldorf, 2003, pp. 135-140.

  • S. Gangopadhyay T.R. Gautam A. Das Gupta (1999) ArticleTitleSubsurface characterisation using artificial neural network and GIS J. Comp. Civil Eng. 13 153–161 Occurrence Handle10.1061/(ASCE)0887-3801(1999)13:3(153)

    Article  Google Scholar 

  • Garson G.D. (1991). Interpreting neural-networks connection weights AI. Expert. 6: 47-51

    Google Scholar 

  • A.T.C. Goh (1995a) ArticleTitleBack-Propagation neural networks for modelling complex systems Artificial Intelligence Eng. 9 143–151 Occurrence Handle10.1016/0954-1810(94)00011-S

    Article  Google Scholar 

  • A.T.C. Goh (1995b) ArticleTitleSeismic liquefaction potential Assessed by neural networks J. Geotech. Eng. 120 IssueID9 1467–1480

    Google Scholar 

  • E. Hoek J.W. Bray (1981) Rock Slope Engineering EditionNumber3 Institution of Mining and Metallurgy London

    Google Scholar 

  • J.A. Hudson (1992a) ArticleTitleAtlas of rock engineering mechanisms Part 2 Slopes. Int. J. Rock Mech. Mining Sci. 29 157–159 Occurrence Handle10.1016/0148-9062(92)92125-V

    Article  Google Scholar 

  • J.A. Hudson (1992b) Rock Engineering systems - Theory and Practice Ellis Horwood Limited WestSussex

    Google Scholar 

  • Hush Don R., Horne B.G. (1999). What’s new since Lippman?. IEEE Signal Process. Magazine. 9-39

  • Janbu, N.: Application of composite slip circles for stability analysis, In: Proc. fourth European Conference on stability of earth slopes, Vol. 3, 1954, pp. 43-49.

  • L. Jing (2003) ArticleTitleA review of techniques, advances and outstanding issues in numerical modelling for rock mechanics and rock engineering Int. J. Rock Mech. Mining Sci. 40 283–353 Occurrence Handle10.1016/S1365-1609(03)00013-3

    Article  Google Scholar 

  • L. Jing J.A. Hudson (2002) ArticleTitleNumerical methods in rock mechanics Int. J. Rock Mech. Mining Sci. 39 409–427 Occurrence Handle10.1016/S1365-1609(02)00065-5

    Article  Google Scholar 

  • Lin, P.S., Lin M.H., Su M.B., Lee T.M.: An investigation on the failure of a building constructed on hillslope, In: Bonnard (ed.) Landslides, Balkema, Vol. 1, 1988, pp. 445-449

  • Madzic, E.: Stability of unstable final slope in deep open iron mine, In: Bonnard (ed.) Landslides, Balkema, Vol. 1, 1988, pp. 455-458

  • D.L Millar J.A. Hudson (1994) ArticleTitlePerformance monitoring of rock engineering systems utilizing neural networks Trans. Inst. Mining Metall. Section A - Mining Ind. 103 A13–A16

    Google Scholar 

  • Y.M. Najjar I.A. Basheer (1996) ArticleTitleUtilising computational neural networks for evaluating the permeability of compacted clay liners Geotech. Geol. Eng. 14 193–212

    Google Scholar 

  • Neural Network Toolbox User’s Guide, The Math Works Inc.Nilson, N.J.: Artificial Intelligence a New Synthesis, Morgan Kaufmann Publishers Inc., SanFrancisco, CA, 1998

  • F. Rosenblatt (1958) ArticleTitleThe perceptron: A probabilistic model for information storage and organization in the brain Psychol. Rev. 65 386–408 Occurrence Handle1:STN:280:CyaD3s7ksFE%3D Occurrence Handle13602029

    CAS  PubMed  Google Scholar 

  • Roussos E. (2000). Neural Networks for Landslide Hazard Estimation. Master Thesis, Kings College

  • N.K. Sah P.R. Sheorey L.W. Upadhyama (1994) ArticleTitleMaximum likelihood estimation of slope stability Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 31 47–53 Occurrence Handle10.1016/0148-9062(94)92314-0

    Article  Google Scholar 

  • M.G. Sakellariou M.D. Ferentinou (2001) ArticleTitleGIS-based estimation of slope stability Nat. Hazard Rev. 2 12–21 Occurrence Handle10.1061/(ASCE)1527-6988(2001)2:1(12)

    Article  Google Scholar 

  • Sakellariou, M.G., Ilias P.: Application of neural networks in the estimation of slope stability, In: Proc. Third Hellenic Geotechnical Conference, (in Greek), Vol 2, 1997, pp.269-276.

  • S.K. Sarma (1975) ArticleTitleSeismic stability of earth dams and embankments Geotechnique. 25 IssueID4 743–761

    Google Scholar 

  • Sklavounos, P., Sakellariou, M.: Intelligent classification of rock masses, In: Adey, Rzevski and Tasso (eds.), Applications of Artificial Intelligence in Engineering, X, Proc. of the Tenth International Conference on Applications of artificial Intelligence in Engineering,Udine, July 1995, Computational Mechanics Publications, pp. 387-393.

  • E. Spencer (1967) ArticleTitleA method of analysis of the stability of embankments assuming parallel inter-slice forces Geotechnique. 17 11–26

    Google Scholar 

  • Stump, D.E., Vallejo L.E., Bazan-Arias N.C.: Analysis of the creeping behavior of a natural slope in Kentucky, In: Keefer and Ho (eds.), Landslides under static and dynamic conditions - Analysis, monitoring and mitigation, ASCE Geotechnical Special Report No.52, 1995, pp. 21-36.

  • J.A. Stegem A. Buenfild (1999) ArticleTitleGlossary of basic neural network terminology for regression problems Neural Comp. Appl. 8 290–296 Occurrence Handle10.1007/s005210050034

    Article  Google Scholar 

  • D.W. Taylor (1937) ArticleTitleStability of earth slopes J. Boston Soc. Civil Eng. 24 197

    Google Scholar 

  • Toll, D.: Artificial Intelligence Systems for geotechnical engineering with specific reference to the ground improvement, In: Proc. Tenth European Young Geotechnical Engineers Conference, Izmir, Turkey, 1996.

  • Y. Yang M.S. Rosenbaum (2001) ArticleTitleThe artificial neural network as a tool for assessing geotechnical properties Geotech. Geol. Eng. 20 149–168 Occurrence Handle10.1023/A:1015066903985

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Issue Date:

  • DOI: