Prediction of Permanent Earthquake-Induced Deformation in Earth Dams and Embankments Using Artificial Neural Networks

Document Type: Research Papers


1 Assistant professor, Department of Civil Engineering, Yazd University, Yazd, Iran

2 M.Sc. Student, Department of Civil Engineering, Yazd University, Yazd, Iran.


This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation of the earth dams and embankments a Multi-Layer Perceptron (MLP) analysis was used. A four-layer, feed-forward, back-propagation neural network, with a topology of 7-9-7-1 was found to be optimum. The results showed that an appropriately trained neural network could reliably predict permanent earthquake-induced deformation of the earth dams and embankments.


Main Subjects

Baziar, M.H. and Ghorbani, A. (2005). “Evaluation of lateral spreading using artificial neural networks”, Soil Dynamics and Earthquake Engineering, 25(1), 1-9.
Behnia, D., Ahangari, K., Noorzad, A. and Moeinossadat S.R. (2013). “Predicting crest settlement in concrete face rock fill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods”, Journal of Zhejiang University-Science A (Applied Physics & Engineering), 14(8), 589-602.
Bray, J.D. and Travasarou, T. (2007). “Simplified procedure for estimating earthquake-induced deviatoric slope displacements”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 133(4), 381-392.
Cybenko, G.V. (1989). “Approximation by superpositions of a sigmoidal function”, Control Signal System (MCSS), 2(4), 303–314.
Das, S. K. and Basudhar, P.K. (2006). “Undrained lateral load capacity of piles in clay using artificial neural network”, Computers and Geotechnics, 33(8), 454-459.
Day, R.W. (2002). Geotechnical earthquake engineering handbook, McGraw-Hill, New York.
Erzin, Y. and Cetin, T. (2013). “The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions”, Computers and Geotechnics, 51, 305-313.
Ferentinou, M.D. and Sakellariou, M.G. (2007). “Computational intelligence tools for the 

prediction of slope performance”, Computers and Geotechnics, 34(5), 362-384.
Flood, I. and Kartam, N. (1994). “Neural network in civil engineering. I: principles and understanding”, Journal of Computing in Civil Engineering, 8(2), 131-148.
Gholamnejad, J. and Tayarani, N. (2010). “Application of artificial neural networks to the prediction of tunnel boring machine penetration rate”, Mining Science and Technology, 20(5), 727-733.
Hanna, A.M., Ural, D. and Saygili, G. (2007). “Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data”, Soil Dynamics and Earthquake Engineering, 27(6), 521-540.
Hertz, J., Krogh, A. and Palmer, R.G. (1991). Introduction to the theory of neural computation, Addison-Wesley, California.
Hynes-Griffin, M.E. and Franklin, A.G. (1984). “Rationalizing the seismic coefficient method”, Geotechnical Laboratory, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Mississippi, 84-93.
Javadi, A., Rezania, M. and Mousavi, N.M. (2006). “Evaluation of liquefaction induced lateral displacements using genetic programming”, Computers and Geotechnics, 33(4-5), 222-233.
Jibson, R.W. (2007). “Regression models for estimating coseismic landslide displacement”, Engineering Geology, 91(4), 209-218.
Kim, Y.S. and Kim B.T. (2008). “Artificial neural network model”, Computers and Geotechnics, 35, 313–322.
Mahdevari, S. and Torabi, S.R. (2012). “Prediction of tunnel convergence using artificial neural networks”, Tunnelling and Underground Space Technology, 28, 218–228.
Maier, H.R., and Dandy, G.C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications”, Environmental Modeling & Software, 15, 101-124.
Makdisi, F.I. and Seed, H.B. (1978). “Simplified procedure for estimating dam and embankment earthquake-induced deformations”, Journal of Geotechnical Engineering Division, ASCE, 104(7), 849-867.
Marandi, S.M., Vaezinejad, S.M. and Khavari, E. (2012). “Prediction of concrete faced rock fill dams settlements using genetic programming algorithm”, International Journal of Geosciences, 3, 601-609.
Masters, T. (1993). Practical neural network recipes in C++, Academic Press, San Diego, California.
Mata, J. (2011). “Interpretation of concrete dam behavior with artificial neural network and multiple linear regression models”, Engineering Structures, 33, 903–910.
Matsumoto, N. (2002). “Evaluation of permanent displacement in seismic analysis of fill dams”, In Proceedings of 3rd US-Japan Workshop on Advanced Research on Earthquake Engineering for Dams, San Diego, 22-23.
Miao, X., Chu, J., Zhang, L. and Qiao J. (2013). “An evolutionary neural network approach to simple prediction of dam deformation”, Journal of Information & Computational Science, 10(5), 1315–1324.
Mohammadi, M., Barani, G.A., Ghaderi, K. and Haghighatandish, S. (2013). “Optimization of earth dams clay core dimensions using evolutionary algorithms”, European Journal of Experimental Biology, 3(3), 350-361.
Mohammadi, N. and Mirabedini, S.J. (2014). “Comparison of particle swarm optimization and backpropagation algorithms for training feed forward neural network”, Journal of Mathematics and Computer Science, 12, 113-123.
Newmark, N.M. (1965). “Effects of earthquakes on dams and embankments”, Geotechnique, 15(2), 139-160.
Park, H. (2011). “Study for application of artificial neural networks in geotechnical problems”, Artificial Neural Networks-Application, Available from:
Pezeshk, S., Camp, C.V. and Karprapu, S. (1996). “Geophysical log interpretation using neural network”, Journal of Computing in Civil Engineering, 10, 136–142.
Rumelhart, D.E., Hinton, G.E. and Mclellend, J.L. (1986). A general framework for parallel distribution processing parallel distribution processing, MIT Press, Cambridge.
Sarma, S.K. (1975). “Seismic stability of earth dams and embankments”, Geotechnique, 25(4), 743-761.
Saygili, G., Rathje, E.M. (2008). “Empirical predictive models for earthquake-induced sliding displacements of slopes”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 134(6), 790-803.
Shahin, M.A. (2008). “Modeling axial capacity of pile foundations by intelligent computing”, Proceedings of the BGA International Conference on Foundations, Dundee, Scotland.
Shahin, M.A., Jaksa, M. and Maier, M. (2008). “State of the art of artificial neural networks in geotechnical Eengineering”, Electronic Journal of Geotechnical Engineering, 8, 1-26.
Shahin, M.A., Jaksa, M.B. and Maier H.R. (2001). “Artificial neural network applications in geotechnical engineering”, Australian Geomechanics, 36(1), 49-62.

Singh, R. and Debasis, R. (2009). “Estimation of earthquake-induced crest settlements of embankments”, American Journal of Engineering and Applied Sciences, 2(3), 515-525.
Singh, R., Debasis, R. and Das, D. (2007). “A correlation for permanent earthquake-induced deformation of earth embankment”, Engineering Geology, 90, 174-185.
Swaisgood, J.R. (2003). “Embankment dam deformations caused by earthquakes”, Pacific Conference on Earthquake Engineering, Cheristcherch, New Zealand.
Swaisgood, J.R. and Au-Yeung, Y. (1991). “Behavior of dams during the 1990 Philippines earthquake”, ASDSO Annual Conference, San Diego.
Tsompanakis, Y., Lagaros, N., Psarropoulos, P. and Georgopoulos E. (2009). “Simulating the seismic response of embankments via artificial neural networks”, Advances in Engineering Software, 40, 640–651.
Yoo, C. and Kim, J. (2007). “Tunneling performance prediction using an integrated GIS and neural network”, Computers and Geotechnics, 34(1), 19-30.
Zhao, H. (2008). “Slope reliability analysis using a support vector machine”, Computers and Geotechnics, 35(3), 459-467.