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Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey

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Abstract

In this study, the calculability of slope stability using the artificial neural networks (ANN) method was examined. Initially, 100 synthetic slope models were created to be used in calculations and the factors of safety of these slopes were calculated by a conventional stability calculation method using slope parameters. Then, factors of safety were calculated by through ANN method. 80 of the datasets from the generated data were used for training while 20 were used for testing in these calculations with the ANN method. In both conventional calculation of stability and the ANN method, input parameters included slope height, height of the water level, slope angle, unit weight, cohesion and angle of internal friction, while the output parameter was factor of safety (SF). A good level of consistency was obtained between the SFs calculated through the conventional method and the ANN method. Furthermore, SFs were calculated separately via the ANN method by assigning range values to unit weight, cohesion and angle of internal friction from amongst the parameters that affect SF for Giresun landslides (Eastern Turkey). The data obtained in this scope revealed that cohesion was the parameter with the highest level of effect on SF. Consequently, it was established that the factors of safety of slopes could be calculated by means of the ANN method in a rapid and convenient manner, the effects of slope parameters on the factors of safety in landslide were examined and the factors of safety for Giresun landslides were calculated through the ANN method.

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References

  • Bayındır R, Sagiroğlu S, Colak I (2009) An intelligent power factor corrector for power system using artificial neural networks. Electr Power Syst Res 79:152–160

    Article  Google Scholar 

  • Beer G, Watson JO (1992) Introduction to finite and boundary element methods for engineers. Wiley, Chichester

    Google Scholar 

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

    Article  Google Scholar 

  • Brady BHG (1979) A direct formulation of the boundary element method of stress analysis for complete plane strain. Int J Rock Mech Min Sci 16(4):235–244

    Article  Google Scholar 

  • Brady BHG (1987) Boundary element and linked methods for underground excavation design. In: Brown ET (ed) Analytical and computational methods in engineering rock mechanics. Allen and Unwin, London, pp 164–204

    Google Scholar 

  • Chen Z, Maun JC (2000) Artificial neural network approach to single-ended fault locator for transmission lines, power systems. IEEE Trans Power Syst 15(1):370–375

    Article  Google Scholar 

  • Cordes E, Fehrenbacher G, Schutz R, Sprunck M, Hahn K, Hofmann R, Biersack JP, Wahl W (1998) Nuclear science. IEEE Trans Nucl Sci 45(3):1464–1469

    Article  Google Scholar 

  • Cundall PA (1971) A computer model for simulating progressive, large scale movements in blocky rock systems, in Rock Fracture. In: Proceedings International Symposium on Rock Mechanics. Nancy, France

  • Cundall PA (1987) Distinct element models of rock and soil structure. In: Brown ET (ed) Analytical an computational methods in engineering rock mechanics. Allen and Unwin, London, pp 129–163

    Google Scholar 

  • Demuth H, Beale M (2000) Neural network toolbox for use with matlab. User’s guide 4 The MathWorks

  • Detournay C, Hart R (1999) FLAC and numerical modeling in geomechanics. In: Hart R (ed) Proceedings, Conference, Minneapolis. Balkema, Rotterdam

    Google Scholar 

  • Domenech C, Wehr T (2011) Use of artificial neural networks to retrieve toa sw radiative fluxes for the earthcare mission. IEEE Trans Geosci Remote Sens 49(6):1839–1849

    Article  Google Scholar 

  • Fellenius W (1927) Erdstatishe berechmungen mit reibungund kohesion. Ernest Verlag, Berlin

    Google Scholar 

  • Güven İH (1993) Doğu Pontidlerin 1/25 000 ölçekli jeolojisi ve komplikasyonu. MTA Dergisi Yayınları Ankara, Turkey (in Turkish)

    Google Scholar 

  • Hart RD, Detournay C, Cundall PA (2008) Continuum and distinct element modeling in geo-engineering. In: Cundall PA (ed) Proceedings First FLAC/DEM Symposium 25 (27August, Minneapolis, USA). Itasca Consulting Group Inc., Minneapolis, p 698

    Google Scholar 

  • Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York

    Google Scholar 

  • Itasca (2011) FLAC ver 6.0, FLAC3D ver 4.0, FLAC/Slope ver 6.0, UDEC ver 5.0 & 3DE Ver 4.1. Itasca Consulting Group Inc., Minneapolis

  • Jain S, Ali MM (2006) Estimation of sound speed profiles using artificial neural networks. Geosci Remote Sens Lett IEEE 3(4):467–470

    Article  Google Scholar 

  • Janbu N (1954) Application of composite slip circles for stability analysis. Proc Eur Conf Stab Earth Slopes 4:43–49

    Google Scholar 

  • Janbu N (1973) Slope stability computations. In: Embankment Dam Engineering, Casagrande memorial volume. Wiley, New York

  • Janbu N, Bjerrum L, Kjaernsli B (1956) Veileding ved losning av fundamentering-soppgaver. Publication no. 16, Norwegian Geotechnical Institute

  • Jemei S, Hissel D, Pera MC, Kauffmann JM (2008) A new modeling approach of embedded fuel-cell power generators based on artificial neural network. Ind Electron IEEE Trans 55(1):437–447

    Article  Google Scholar 

  • Lou SLA, Lin JS, Freedman MT, Chien MV, Mun SK (1995) Artificial convolution neural network techniques and applications for lung nodule detection. Med Imaging IEEE Trans 14(4):711–718

    Article  Google Scholar 

  • Ming Z (2011) A neural network application in predicting slope stability. In: International Conference on Electric Information and Control Engineering (ICEICE)

  • Minnix JI, McVey ES, Inigo RM (1992) A multilayered self-organizing artificial neural network for invariant pattern recognition. Knowl Data Eng IEEE Trans 4(2):162–167

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Nalina P, Meenambal T, Sathyanarayan-Sridhar R (2014) Slope stability analysis of Kallar-Coonoor hill road stretch of the nilgiris. J Comput Sci 10(7):1107–1114

    Article  Google Scholar 

  • Ostlin E, Zepernick H, Suzuki H, Macrocell P (2010) Path-loss prediction using artificial neural networks. Veh Technol IEEE Trans 59(6):2734–2747

    Article  Google Scholar 

  • Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 4:001–015

    Google Scholar 

  • Sağıroğlu Ş, Çolak İ, Bayındır R (2006) Power factor correction technique based on artificial neural networks. Energy Convers Manag 47(18–19):3204–3215

    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 

  • Sarma SK (1973) Stability analysis of embankments and slopes. Geotechnique 23(3):423–433

    Article  Google Scholar 

  • Sarma SK (1979) Stability analysis of embankments and slopes. ASCE J Geotech Eng Div 105:1511–1524

    Google Scholar 

  • Spencer E (1967) A method of analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique 17:2–26

    Article  Google Scholar 

  • Taylor DW (1937) Stability of Earth Slopes. Control Soil Mechanics for the Society of Civil Engineers, Boston

    Google Scholar 

  • Tolon M (2007) Artificial neural network approaches for slope stability. Dissertation, ITU Institute of Science and Technology, Istanbul

  • Venkatesan R, Balamurugan B (2001) A real-time hardware fault detector using an artificial neural network for distance protection. Power Deliv IEEE Trans 16(1):75–82

    Article  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 

  • Zhou K, Chen Z (2009) Stability prediction of tailing dam slope based on neural network pattern recognition. In: Second International Conference on Environmental and Computer Science. Dubai

  • Zhou ZH, Jiang Y (2003) Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. Inf Technol Biomed IEEE Trans 7(1):37–42

    Article  Google Scholar 

  • Zhu Y, Ding X, Li Z, Zhou S (2010) Analysis on uranic slope stability based on neural network. Int Forum Inf Technol Appl, China

    Book  Google Scholar 

Download references

Acknowledgments

This research was supported by Karadeniz Technical University research funding (No: 8781).

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Authors and Affiliations

  1. Department of Geophysics, Karadeniz Technical University, 61080, Trabzon, Turkey

    Kenan Gelisli, Türkan Kaya & Ali Erden Babacan

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  1. Kenan Gelisli
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  2. Türkan Kaya
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  3. Ali Erden Babacan
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Correspondence to Ali Erden Babacan.

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Gelisli, K., Kaya, T. & Babacan, A.E. Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey. Environ Earth Sci 73, 8639–8646 (2015). https://doi.org/10.1007/s12665-015-4027-1

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  • DOI: https://doi.org/10.1007/s12665-015-4027-1

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