Artificial Neural Networks Approach for Earthquake Deformation Determination of Geosynthetic Reinforced Retaining Walls

Authors

  • Tahir Erdem Ozturk Boğaziçi University

Keywords:

Finite Element Analysis, Artificial Neural Network, reinforced Soil wall

Abstract

Back-to-back Mechanically Stabilized Earth (MSE) walls are commonly used for bridge approach embankments.  Artificial Neural Network (ANN) analysis conducted in this study was applied for the first time in literature to estimate the seismic-induced permanent displacements of retaining walls under dynamic loads. For this purpose, a parametric study of seismic response analysis of reinforced soil retaining structures was performed to train the ANN using finite element analysis. The variables used to define wall geometry were reinforcement length, reinforcement spacing, wall height and facing type. The harmonic motion had three different levels of peak ground accelerations, namely 0.2g, 0.4g and 0.6g and had a duration of 6 sec with a frequency of 3 Hz.  Although developing an analytical or empirical model is feasible in some simplified situations, most data manufacturing processes are complex and, therefore, models that are less general, more practical and less expensive than the analytical models are of interest. The agreement of the neural network predicted displacements and deformation classification with Finite Element Analyses results were encouraging by the means of correlation since the coefficient values of R=0.99 for ANN regression analysis were achieved.

Downloads

Download data is not yet available.

Author Biography

Tahir Erdem Ozturk, Boğaziçi University

Civil Engineer, Dr.

References

Anastasopoulos I., T., Georgarakos, V., Georgiannou, V., Drosos, R., Kourkoulis, 2010, “Seismic performance of bar-mat reinforced-soil retaining wall: Shaking table testing versus numerical analysis with modified kinematic hardening constitutive model”, Soil Dynamics and Earthquake Engineering, 10.1016/j.soildyn.2010.04.020 journal homepage: www.elsevier.com/locate/soildyn

Basheer, I.A., 2002, “Stress-strain behavior of geomaterials in loading reversal simulated by time-delay neural networks”, Journal of Materials in Civil Engineering, 14(3), 270273.

Bathurst, R.J., Hatami, K., 1998, “Seismic response analysis of a geosynthetic-reinforced soil wall”, Geosynthetics Internatinal, 5(1-2):127-166.

Benardos, A.G. and D.C., Kaliampakos, 2004, “Modeling TBM performance with artificial neural networks”, Tunneling and Underground Space Technology, 19(6), 597-605.

Fausett, L.V. 1994, “Fundamentals neural networks: Architecture, algorithms,and applications”, Prentice-Hall, Englewood Cliffs, New Jersey.

Goh, A.T.C., K.S., Wong and B.B., Broms, 1995, “Estimation of lateral wall movements in braced excavation using neural networks”, Canadian Geotechnical Journal, 32, 1059-1064.

Guler, E., E.Cicek M., Hamderi, M.M., Demirkan, 2011, “Numerical analysis of reinforced soil walls with granular and cohesive backfills under cyclic loads”, Bull Earthquake Eng.DOI 10.1007/s10518-011-9322-y

Haykin, S.S., 1999, Neural networks : a comprehensive foundation. Upper Saddle River, N.J., Prentice Hall.

Kim, D.H., D.J., Kim, et al., 1999, “The application of neural networks and statistical methods to process design in metal forming processes”, International Journal of Advanced Manufacturing Technology 15(12): 886-894.

Kim, Y. and B., Kim, 2008, “Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model”, Computers and Geotechnics, 35(3), 313-322.

Kramer, S.L., 1996, Geotechnical Earthquake Engineering, Prentice Hall, New Jersey.

Kung, G.T., E.C., Hsiao, M., Schuster and C.H., Juang, 2007, “A neural network approach to estimating deflection of diaphram walls caused by excavation in clays” Computers and Geotechnics, 34(5), 385-396.

Ling, H.I., Y., Mohri, D., Leshchinsky, C., Burke, K., Matsushima, H., Liu, 2005 “Large-scale shaking table tests on modular-block reinforced soil retaining walls”, J Geotech Geoenviron Eng ASCE 131(4):465-476

Lu, Y. 2005, “Underground blast induced ground shock and its modeling using artificial neural network”, Computers and Geotechnics, 32(3), 164-178.

Shang, J.Q., W., Ding, R.K., Rowe and L., Josic, 2004, “Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks”, Canadian Geotechnical Journal, 41(6), 1054-1067.

Uang, C.H. and V.V., Bertero, 1988, Implications of recorded earthquake ground motions on seismic design of buildings structures, UCB/EERC-88/13, California.

Ural, D.N. and H., Saka, 1998, “Liquefaction assessment by neural networks”, Electronic Journal of Geotechnical Engineering, http://www.ejge.com/Ppr9803/ Ppr9803.htm

Yoo, C. and J., Kim, 2007, “Tunneling performance prediction using an integrated GIS and neural network”, Computers and Geotechnics, 34(1), 19-30.sa

Young-Su, K. and K., Byung-Tak, 2006, “Use of artificial neural networks in the prediction of liquefaction resistance of sands”, Journal of Geotechnical and Geoenvironmental Engineering, 132(11), 1502-1504.

Zevgolis, I., 2007, “Numerical and Probabilistic Analysis of Reinforced Soil Structures”, PhD Thesis, Purdue University.

Zhu, J.H., M.M., Zaman and S.A., Anderson, 1998a, “Modeling of soil behavior with a recurrent neural network”, Canadian Geotechnical Journal, 35(5), 858-872.

Zhu, J.H., M.M., Zaman and S.A., Anderson, 1998b, “Modeling of shearing behavior of a residual soil with recurrent neural network”, International Journal of Numerical and Analytical Methods in Geomechanics, 22(8), 671-687.

Zurada, J.M., 1992, Introduction to artificial neural systems, West Publishing Company, St. Paul.

Downloads

Published

25.02.2014

How to Cite

Ozturk, T. E. (2014). Artificial Neural Networks Approach for Earthquake Deformation Determination of Geosynthetic Reinforced Retaining Walls. International Journal of Intelligent Systems and Applications in Engineering, 2(1), 1–9. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/78

Issue

Section

Research Article