Application of Artificial Neural Network in Assessing Fill Slope Stability
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33085
Application of Artificial Neural Network in Assessing Fill Slope Stability

Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

Abstract:

This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: Landslide, limit analysis, ANN, soil properties.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315727

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1205

References:


[1] J. M. Duncan, “State of the art: Limit equilibrium and finite-element analysis of slopes,” J. Geotech. Engrg., vol. 122, no. 7, pp. 577–596, Jul 1996.
[2] M. Chang, “A 3D slope stability analysis method assuming parallel lines of intersection and differential straining of block contacts,” Can. Geotech. J., vol. 39, no, 4, pp. 799–811, Aug 2002.
[3] R. Baker, R. Shukha, V. Operstein, and S. Frydman, “Stability charts for pseudo-static slope stability analysis,” Soil. Dyn. Earthquake Eng., vol. 26, no. 9, pp. 813–823, Sep 2006.
[4] K. Lim, A. V. Lyamin, M. J. Cassidy, and A. J. Li, “Three-dimensional slope stability charts for frictional fill materials placed on purely cohesive clay,” Int. J. Geomech., vol. 16, no. 2, Apr 2016.
[5] B. Indraratna, A. Balasubramaniam, and S. Balachandran, “Performance of test embankment constructed to failure on soft marine clay,” J. Geotech. Engrg., vol. 118, no. 1, pp. 12-33, Jan 1992.
[6] A. S. Al-Homoud, A. B. Tal, and S.A. Taqieddin, “A comparative study of slope stability methods and mitigative design of a highway embankment landslide with a potential for deep seated sliding,” Eng. Geology, vol. 47, no. 1-2, pp. 157-173, Aug 1997.
[7] D. W. Taylor, “Stability of earth slopes,” J. Boston Soc. Civ. Eng., vol. 24, pp. 197–246, 1937.
[8] K. Lim, M. J. Cassidy, A. J. Li, and A. V. Lyamin, “Mean parametric monte carlo study of fill slopes,” Int. J. Geomech., (http://dx.doi.org/10.1061/(ASCE)GM.1943-5622.0000812).
[9] M. A. Shahin, M. B. Jaksa, and H. R. Maier, “Artificial neural network applications in geotechnical engineering,” Australian Geomechanics, vol. 36, no. 1, pp. 49-62, Mar 2001.
[10] F. Silva, T.W. Lambe, and W.A. Marr, “Probability and risk of slope failure,” J. Geotech. Geoenviron. Eng., vol. 134, no. 12, pp. 1691-1699, Dec 2008.
[11] A. J. Li, M. J. Cassidy, Y. Wang, R. S. Merifield, and A. V. Lyamin, “Parametric Monte Carlo studies of rock slopes based on the Hoek–Brown failure criterion,” Comput. Geotech., vol. 45, pp. 11–18, Sep 2012.
[12] K. J. Shou, and C. F. Wang, “Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan,” Eng. Geol., vol. 68, no. 3–4, pp. 237–250, Mar 2003.
[13] K. Lim, A. J. Li, A. Schmid, and A. V. Lyamin “Slope-stability assessments using finite-element limit analysis methods,” Int. J. Geomech., vol. 17, no. 2, Feb 2017.
[14] D. Griffiths, and R. Marquez, “Three-dimensional slope stability analysis by elastoplastic finite elements,” Geotechnique, vol. 57, no. 6, pp. 537–546, Aug 2007.
[15] M. T. Manzari, and M. A. Nour, “Significance of soil dilatancy in slope stability analysis,” J. Geotech. Geoenviron. Eng., vol. 26, no. 1, pp. 75–80, Jan 2000.
[16] A. V. Lyamin, and S. W. Sloan, “Lower bound limit analysis using non-linear programming,” Int. J. Numer. Methods. Eng., vol. 55, no. 5, pp. 573–611, Jul 2002.
[17] A. V. Lyamin, and S. W. Sloan, “Upper bound limit analysis using linear finite elements and non-linear programming,” Int. J. Numer. Anal. Methods Geomech., vol. 26, no, 2, pp. 181–216, Feb 2002.
[18] K. Krabbenhoft, A. V. Lyamin, M. Hjiaj, and S. W. Sloan, “A new discontinuous upper bound limit analysis formulation,” Int. J. Numer. Methods. Eng., vol. 63, no. 7, pp. 1069–1088, Jun 2005.
[19] S. J. Lee, S. R. Lee, and Y. S. Kim, “An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation,” Comput. Geotech., vol. 30, no. 6, pp. 489–503, Sep 2003.
[20] H. Sonmez, C. Gokceoglu, H. A. Nefeslioglu, and A. Kayabasi, “Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation,” Int. J. Rock Mech. Min. Sci., vol. 43, no. 2, pp. 224–235, Feb 2006.
[21] M. A. Shahin, and M. B. Jaksa, “Neural network prediction of pullout capacity of marquee ground anchors,” Comput. Geotech., vol. 32, no. 3, pp. 153–163, Apr 2005.
[22] Y. L. Kuo, M. B. Jaksa, A. V. Lyamin, and W. S. Kasswa, “ANN-based model for predicting the bearing capacity of strip footing onmulti-layered cohesive soil,” Comput. Geotech., vol. 36, no. 3, pp. 503–516, Apr 2009.
[23] R. Boubou, F. Emeriault, and R. Kastner, “Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling,” Can. Geotech. J., vol. 47, no. 11, pp. 1214–1233, Nov 2010.
[24] G. T. C. Kung, E. C. L. Hiao, M. Schuster, and C. H. Huang, “A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays,” Comput. Geotech., vol, 34 no. 5, pp. 385–396, Sep 2007.
[25] S. E. Cho, “Probabilistic stability analyses of slopes using the ANN-based response surface,” Comput. Geotech., vol. 36, no. 5, pp. 787–797, Jun 2009.
[26] A. J. Li, S. Khoo, A. V. Lyamin, Y. Wang, “Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm,” Automation in Construction, vol. 65, pp.42–50, May 2016.
[27] M. Sari, “The stochastic assessment of strength and deformability characteristic s for a pyroclastic rock mass,” Int. J. Rock Mech. Min. Sci., vol. 46, no. 3, pp. 613–626, Apr 2009.
[28] J. A. Abdalla, M. F. Attom, and R. Hawileh, “Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network,” Environmental Earth Sciences, vol. 73, no. 9, pp. 5463-5477, May 2015.
[29] K. Gelisli, T. Kaya, and A. E. Babacan, “Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey,” Environmental Earth Sciences, vol. 73, no. 12, pp. 8639-8646, Jun 2015.
[30] J. Shiau, R. S. Merifield, A. V. Lyamin, and S. W. Sloan “Undrained stability of footings on slopes,” Int. J. Geomech., vol. 11, no. 5, pp. 381–290, Oct 2011.
[31] J. Kim, R. Salgado, and H. Yu “Limit analysis of soil slopes subjected to pore-water pressures.” J. Geotech. Geoenviron. Eng., vol. 125, no. 1, pp. 49–58, Jan 1999.
[32] D. Loukidis, P. Bandini, and R. Salgado. “Stability of seismically loaded slopes using limit analysis,” Geotechnique, vol. 53, no. 5, pp. 463–480, Jun 2003.
[33] Z. Qian, A. J. Li, R. S. Merifield, and A. V. Lyamin “Slope stability charts for two-layered purely cohesive soils based on finite-element limit analysis methods,” Int. J. Geomech., vol. 15, no. 3, Jun 2015.
[34] G. B. Huang, L. Chen, and C. Siew “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, Jul 2006.