Predictive modeling of static and seismic stability of small homogeneous earth dams using artificial neural network

  • A. ZeroualEmail author
  • A. Fourar
  • M. Djeddou
Original Paper


The previous results of artificial neural network (ANN) prediction models in natural slope stability are encouraging, which gives logical hope for the practical application of these models for earth dams. In this context, this study presents an ANN model which allows the user to get four factors of safety (FS(i)) of small earth dams under long-term stability condition with static or earthquake loading immediately. Safety factors (FS(i)) for the model are calculated by carrying out two computations each for two cases, that is, earth dam subjected to full reservoir steady-state seepage condition with and without earthquake (FS(F + EQ) and FS(F)) and earth dam empty reservoir with and without earthquake (FS(Em + EQ) and FS(Em)). A database of 1372 different inputs and 4 outputs was built through strength reduction finite element method (SR-FEM). The used ANN is a feed-forward back-propagation neural network (FBNN) with three layers. The most appropriate FBNN architecture was found 11–21–4, as this gave the best FS(i) prediction with the lowest error. The relative importance of the inputs parameters is studied using both Garson’s algorithm and connection weight approach. Moreover, for further verification, the developed model has been used for prediction FS(i) of new earth dam datasets. The predicted results have been compared with the obtained ones from different limit equilibrium (LE) slope stability computations. The comparison had confirmed a very satisfactory capability of the ANN model to predict the FS(i).


Small earth dams Factor of safety Pseudostatic stability Artificial neural network Strength reduction method 


  1. Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min Sci 61:86–95CrossRefGoogle Scholar
  2. Alemdag S, Gurocak Z, Cevik A, Cabalar AF, Gokceoglu C (2016) Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Eng Geol 203:70–82CrossRefGoogle Scholar
  3. Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Geotechnique 5(1):7–17CrossRefGoogle Scholar
  4. Casagrande A (1937) Seepage through dams. J. New England Water Works Association, reprinted in Contribution to soil mechanics 1925-1940. Boston Society of Civil Engineers, Boston, p 1940Google Scholar
  5. Cevik A (2011) Modeling strength enhancement of FRP confined concrete cylinders using soft computing. Expert Syst Appl 38(5):5662–5673CrossRefGoogle Scholar
  6. Cevik A, Kutuk MA, Erklig A, Guzelbey IH (2008) Neural network modeling of arc spot welding. J Mater Process Technol 202(1–3):137–144. CrossRefGoogle Scholar
  7. Ceylan H, Gopalakrishnan K, Kim S (2010) Soil stabilization with bioenergy coproduct. Transp Res Rec 2186:130–137. CrossRefGoogle Scholar
  8. Chahar BR (2004) Determination of length of horizontal drain in homogeneous earth dams. J Irrig Drain Eng 130(6):530–536. CrossRefGoogle Scholar
  9. Cheng YM, Länsivaara T, Wei WB (2007) Two-dimensional slope stability analysis by limit equilibrium and strength reduction methods. Comput Geotech 34(3):137–150CrossRefGoogle Scholar
  10. Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319. CrossRefGoogle Scholar
  11. Das SK (2013) Artificial neural networks in geotechnical engineering: modeling and application issues. In Metaheuristics in water, geotechnical and transport engineering (pp. 231–270)Google Scholar
  12. Das SK, Basudhar PK (2005) Prediction of coefficient of lateral earth pressure using artificial neural networks. Electron J Geotech Eng 10:1Google Scholar
  13. Duncan JM (1996) State of the art: limit equilibrium and finite-element analysis of slopes. J Geotech Eng ASCE 122(7):577–596CrossRefGoogle Scholar
  14. Duncan JM, Wright SG, Brandon TL (2014) Soil strength and slope stability. John Wiley & Sons, Hoboken, NJGoogle Scholar
  15. Erzin Y, Cetin T (2014) The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions. Geomech Eng 6(1):1–15. CrossRefGoogle Scholar
  16. Erzin Y, Nikoo M, Cetin T (2016) The use of self-organizing feature map networks for the prediction of the critical factor of safety of an artificial slope. Neural Network World 5:461–475CrossRefGoogle Scholar
  17. Eurocode8 (2004) Design of structures for earthquake resistance. Part 5: Foundations, retaining structures and geotechnical aspects. EN 1998–1995. European Committee for Standarisation, BrusselsGoogle Scholar
  18. Fattahi H (2017) Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods. J Min Environ 8(2):163–177Google Scholar
  19. Garson GD (1991) Interpreting neural-network connection weights. Artif Intell Expert 6(7):4751Google Scholar
  20. Goh ATC (1995) Back-propagation neural networks for modelling complex systems. Artif Intell Eng 9:143–151CrossRefGoogle Scholar
  21. Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2015) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97. CrossRefGoogle Scholar
  22. Griffiths DV, Lane PA (1999) Slope stability analysis by finite elements. Geotechnique 49(3):387–403. CrossRefGoogle Scholar
  23. Hecht-Nielsen R (1990) Neurocomputing, 1st edn. Prentice Hall, NJGoogle Scholar
  24. Hoang ND, Bui DT (2017) Slope stability evaluation using radial basis function neural network, least squares support vector machines, and extreme learning machine. In Handbook of Neural Computation (pp. 333–344).
  25. ICOLD (2016) Small dams: design, Surveillance and Rehabilitation Bulletin 157Google Scholar
  26. Jibson RW (2011) Methods for assessing the stability of slopes during earthquakes—a retrospective. Eng Geol 122(1–2):43–50. CrossRefGoogle Scholar
  27. Kayabasi A, Yesiloglu-Gultekin N, Gokceoglu C (2015) Use of non-linear prediction tools to assess rock mass permeability using various discontinuity parameters. Eng Geol 185:1–9CrossRefGoogle Scholar
  28. Loukidis D, Bandini P, Salgado R (2003) Stability of seismically loaded slopes using limit analysis. Geotechnique 53(5):463–479CrossRefGoogle Scholar
  29. Luu LH, Veylon G, Mercklé S, Carvajal C, Bard PY (2016) A simplified method for estimating seismic performance of small homogeneous earth dams. In International Symposium Qualification of Dynamic Analyses of Dams and their Equipment and of Probabilistic Assessment of Seismic Hazard in EuropeGoogle Scholar
  30. 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(3):1267–1277. CrossRefGoogle Scholar
  31. Masters T (1993) Practical neural network recipes in C++'. Academic Press Inc. San Diego CA USA Newsgroup comp.Ai.Neural-Nets. Available online:
  32. Nourani V, Ghaffari H (2012) Assessment of slope stability in embankment dams using artificial neural network (case study: Zonouz embankment dam). International Journal of Advances in Civil Engineering and Architecture (IJACEA) 1(1):65–75Google Scholar
  33. Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178(3):389397Google Scholar
  34. Özkan C, Erbek FS (2003) The comparison of activation functions for multispectral Landsat TM image classification. Photogramm Eng Remote Sensing 69(11):1225–1234Google Scholar
  35. Petterson KE (1955) The early history of circular sliding surfaces. Geotechnique 5:275–296CrossRefGoogle Scholar
  36. Sakellariou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23(4):419–445CrossRefGoogle Scholar
  37. Samui P (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56(2):255–267. CrossRefGoogle Scholar
  38. Sarle WS (2002) Neural Network FAQ, part 1 of 7: Introduction, periodic posting to the UsenetGoogle Scholar
  39. Sarma SK (1973) Stability analysis of embankments and slopes. Geotechnique 23(3):423–433Google Scholar
  40. Seed HB (1979) Considerations in the earthquake-resistant design of earth and rockfill dams. Géotechnique 29(3):215–263. CrossRefGoogle Scholar
  41. Sen S, Sezer EA, Gokceoglu C, Yagiz S (2012) On sampling strategies for small and continuous data with the modeling of genetic programming and adaptive neuro-fuzzy inference system. J Intell Fuzzy Syst 23(6):297–304Google Scholar
  42. Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22(1):97–103CrossRefGoogle Scholar
  43. Spencer E (1967) A method of analysis of the stability of embankments assuming parallel interslice forces. Géotechnique 17(1):11–26CrossRefGoogle Scholar
  44. Suman S, Khan SZ, Das SK, Chand SK (2016) Slope stability analysis using artificial intelligence techniques. Nat Hazards 84(2):727–748. CrossRefGoogle Scholar
  45. Tsompanakis Y, Lagaros ND, Psarropoulos PN, Georgopoulos EC (2009) Simulating the seismic response of embankments via artificial neural networks. Adv Eng Softw 40(8):640–651CrossRefGoogle Scholar
  46. Tüysüz C (2010) The effect of the virtual laboratory on the students achievement and attitude in chemistry. Int Online J Educ Sci 2(1):37–53Google Scholar
  47. U S Army Corps of Engineers (2003) Engineering and design-slope stability. Engineering Manual EM 1110–2-1902. Department of the Army, Corps of Engineers, Office of the Chief of Engineers, Washington, DCGoogle Scholar
  48. United States Bureau of Reclamation (USBR) (2003) Design of small dams. Oxford & IBH, New DelhiGoogle Scholar
  49. Yagiz S, Sezer EA, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Methods Geomech 36(14):1636–1650. CrossRefGoogle Scholar
  50. Yesiloglu-Gultekin N, Gokceoglu C, Sezer EA (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int J Rock Mech Min Sci 62:113–122CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of Hydraulic, Faculty of TechnologyUniversity of Batna 2BatnaAlgeria
  2. 2.Research Laboratory in Subterranean and Surface Hydraulics (LARHYSS), Faculty of Sciences and TechnologyMohamed Khider UniversityBiskraAlgeria

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