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Analysis of flow through dam foundation by FEM and ANN models Case study: Shahid Abbaspour Dam

  • Received : 2014.02.12
  • Accepted : 2015.05.21
  • Published : 2015.10.25

Abstract

Three-dimensional simulation of flow through dam foundation is performed using finite element (Seep3D model) and artificial neural network (ANN) models. The governing and discretized equation for seepage is obtained using the Galerkin method in heterogeneous and anisotropic porous media. The ANN is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning, using the water level elevations of the upstream and downstream of the dam, as input variables and the piezometric heads as the target outputs. The obtained results are compared with the piezometric data of Shahid Abbaspour's Dam. Both calculated data show a good agreement with available measurements that demonstrate the effectiveness and accuracy of purposed methods.

Keywords

References

  1. Arun, K.J. and Reddi, L.N. (2011), "Finite-depth seepage below flat aprons with equal end cutoffs", J. Hydraul. Eng, 137(12), 1659-1667. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000459
  2. ASCE Task Committee (2000), "Artificial neural networks in hydrology, II: Hydrologic applications", J. Hydrol. Eng., 5(2), 124-137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
  3. Ataie-Ashtiani, B., Volker, R.E. and Lockington, D.A. (1999), "Numerical and experimental study of seepage in unconfined aquifers with a periodic boundary condition", J. Hydrol., 222(1-4), 165-174. https://doi.org/10.1016/S0022-1694(99)00105-5
  4. Ayoubloo, M.K., Azamathulla, H.Md., Jabbari, E. and Mahjoobi, J. (2011), "Model tree approach for estimation of critical submergence for horizontal intakes in open channel flows", Expert Syst. Appl., 38(8), 10114-10123. https://doi.org/10.1016/j.eswa.2011.02.073
  5. Aziz, A.R.A. and Wong, K.V. (1992), "A neural-network approach to the determination of aquifer parameters", J. Ground Water, 30(2), 164-166. https://doi.org/10.1111/j.1745-6584.1992.tb01787.x
  6. Azamathulla, H.Md., Deo, M.C. and Deolalikar, P.B. (2005), "Neural networks for estimation of scour downstream of ski-jump bucket", J. Hydraul. Eng., 131(10), 898-908. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:10(898)
  7. Azamathulla, H.Md., Deo, M.C. and Deolalikar, P.B. (2006), "Estimation of scour below spillways using neural networks", IAHR, J. Hydraul. Res., 44(1), 61-69. https://doi.org/10.1080/00221686.2006.9521661
  8. Azamathulla, H.Md., Deo, M.C. and Deolalikar, P.B. (2008), "Alternative neural networks to estimate the scour below spillways", Adv. Eng. Software, 39(8), 689-698. https://doi.org/10.1016/j.advengsoft.2007.07.004
  9. Azamathulla, H.Md. and Zakaria, N.A. (2011), "Prediction of scour below submerged pipeline crossing a river using ANN", IWA - Water Sci. Technol., 63(10), 2225-2230. https://doi.org/10.2166/wst.2011.459
  10. Bhatti, M.A. (2005), Fundamental Finite Element Analysis and Applications With Mathematica and Matlab Computations, John Wiley & Sons Inc., Hoboken, NJ, USA.
  11. Caudill, M. and Butler, C. (Eds.) (1987), IEEE First International Conference on Neural Networks, San Diego, CA, USA.
  12. Chang, Y.Ch., Chen, G.Y. and Yeh, H.D. (2010), "Transient flow into a partially penetrating well during the constant-head test in unconfined aquifers", J. Hydraul. Eng., 137(9), 1054-1064.
  13. Childs, E.C. and Collins-George, N. (1950), "The permeability of porous materials", Proc. R. Soc. London, 201(A), 392-405. https://doi.org/10.1098/rspa.1950.0068
  14. Cooley, R.L. (1971), "A finite difference method for unsteady flow in variably saturated porous media: application to a single pumping well", Water Resour. Res., 7(6), 1607-1625. https://doi.org/10.1029/WR007i006p01607
  15. Dolling, O.R. and Varas, E.A. (2002), "Artificial neural networks for streamflow prediction", J. Hydraul. Res., 40(5), 547-554. https://doi.org/10.1080/00221680209499899
  16. Fredlund, D.G. and Rahardjo, H. (1993), Soil Mechanics for Unsaturated Soils, Wiley, Chichester, pp. 136-140.
  17. Geo-Slope International (2001), Seep3D Software (Version 1), Calgary, AL, Canada.
  18. Ghobadi, M.H., Khanlari, G.R. and Djalaly, H. (2005), "Seepage problems in the right abutment of the Shahid Abbaspour", Eng. Geol., 82(2), 119-126. https://doi.org/10.1016/j.enggeo.2005.09.002
  19. Honjo, Y., Giao, P.H. and Naushahi, P.A. (1995), "Seepage analysis of Tarbela dam (Pakistan) using finite element method", Int. J. Rock Mech. Min. Sci. Geomech. Abstr., 32(3), 131A.
  20. Jain, A. and Reddi, L. (2011), "Finite-depth seepage below flat aprons with equal end cutoffs", J. Hydraul. Eng., 137(6), 1659-1668. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000459
  21. Jain, S.K. (2001), "Development of integrated sediment rating curves using ANNs", J. Hydraul. Eng., 127(1), 30-37. https://doi.org/10.1061/(ASCE)0733-9429(2001)127:1(30)
  22. Krikland, M.R., Hills, R.G. and Wierenga, P.J. (1992), "Algorithms for solving Richard's equation for variably saturated soils", Water Resour. Res., 28(8), 2049-2058. https://doi.org/10.1029/92WR00802
  23. Li, L., Barry, D.A. and Pattiaratchi, C.B. (1997), "Numerical modeling of tidal-induced beach water table fluctuations", Coast. Eng., 30(1-2), 105-123. https://doi.org/10.1016/S0378-3839(96)00038-5
  24. Money, R.L. (2006), "Comparison of 2D and 3D Seepage model results for excavation near levee toe", GeoCongress, Atlanta, GA, USA, pp. 1-4.
  25. Nagy, H.M., Watanabe, K. and Hirano, M. (2002), "Prediction of sediment load concentration in rivers using artificial neural network model", J. Hydraul. Eng., 128(6), 588-595. https://doi.org/10.1061/(ASCE)0733-9429(2002)128:6(588)
  26. Panthulu, T.V., Krishnaiah, C. and Shirke, J.M. (2001), "Detection of seepage paths in earth dams using self-potential and electrical resistivity methods", Eng. Geol., 59(3-4), 281-295. https://doi.org/10.1016/S0013-7952(00)00082-X
  27. Rajurkar, M.P., Kothyari, U.C. and Chaube, U.C. (2002), "Artifical neural networks for daily rainfall-runoff modeling", Hydrol. Sci. J., 47(6), 865-878. https://doi.org/10.1080/02626660209492996
  28. Rajurkar, M.P., Kothyari, U.C. and Chaube, U.C. (2004), "Modeling of daily rainfall-runoff relationship with artificial neural network", J. Hydrol., 285(1-4), 96-113. https://doi.org/10.1016/j.jhydrol.2003.08.011
  29. Rubin, J. (1968), "Theoretical analysis of two-dimensional, transient flow of water in unsaturated and partly saturated soils", Soil Sci. Soc. Am. Proc., 32(5), 607-615. https://doi.org/10.2136/sssaj1968.03615995003200050013x
  30. Tayfur, G. (2002), "Artificial neural networks for sheet sediment transport", Hydrol. Sci. J., 47(6), 879-892. https://doi.org/10.1080/02626660209492997
  31. Tayfur, G., Swiatek, D., Wita, A. and Singh, V.P. (2005), "Case study: Finite element method and artificial neural network models for flow through Jeziorsko Earthfill Dam in Poland", J. Hydrol., 131(6), 431-440.
  32. Tien-Kuen, H. (1996), "Stability analysis of an earth dam under steady state seepage", Comput. Struct., 58(6), 1075-1082. https://doi.org/10.1016/0045-7949(95)00230-8
  33. Tokar, A.S. and Johnson, P.A. (1999), "Rainfall-runoff modeling using artificial neural networks", J. Hydrol. Eng., 4(3), 232-239. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(232)
  34. Turkmen, S., Ozguler, E., Taga, H. and Karaogullarindan, T. (2002), "Seepage problems in the karstic limestone foundation of the Kalecik Dam (South Turkey)", Eng. Geol., 63(3-4), 247-257. https://doi.org/10.1016/S0013-7952(01)00085-0
  35. Xu, Y.Q., Unami, K. and Kawachi, T. (2003) "Optimal hydraulic design of earth dam cross section using saturated-unsaturated seepage flow model", Adv. Water Resour., 26(1), 1-7. https://doi.org/10.1016/S0309-1708(02)00124-0

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