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Artificial Neural Network Modeling and Genetic Algorithm Based Optimization of Hydraulic Design Related to Seepage under Concrete Gravity Dams on Permeable Soils
Abstract:Hydraulic structures such as gravity dams are classified as essential structures, and have the vital role in providing strong and safe water resource management. Three major aspects must be considered to achieve an effective design of such a structure: 1) The building cost, 2) safety, and 3) accurate analysis of seepage characteristics. Due to the complexity and non-linearity relationships of the seepage process, many approximation theories have been developed; however, the application of these theories results in noticeable errors. The analytical solution, which includes the difficult conformal mapping procedure, could be applied for a simple and symmetrical problem only. Therefore, the objectives of this paper are to: 1) develop a surrogate model based on numerical simulated data using SEEPW software to approximately simulate seepage process related to a hydraulic structure, 2) develop and solve a linked simulation-optimization model based on the developed surrogate model to describe the seepage occurring under a concrete gravity dam, in order to obtain optimum and safe design at minimum cost. The result shows that the linked simulation-optimization model provides an efficient and optimum design of concrete gravity dams.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339790Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1069
 Harr, M.E., Groundwater and seepage. 1962: Courier Corporation.
 Shahrbanozadeh, M., G.-A. Barani and S. Shojaee, Simulation of flow through dam foundation by isogeometric method. Engineering Science and Technology, an International Journal, 2015. 18(2): p. 185-193.
 Mansuri, B., F. Salmasi and B. Oghati, Effect of Location and Angle of Cutoff Wall on Uplift Pressure in Diversion Dam. Geotechnical and Geological Engineering, 2014. 32(5): p. 1165-1173.
 Lefebvre, G., C. Lupien, J.J. Pare and J.-P. Tournier, Effectiveness of seepage control elements for embankments on semipervious foundations. Canadian Geotechnical Journal, 1981. 18(4): p. 572-576.
 Alsenousi , K.F. and H.G. Mohamed, Effects of inclined cutoffs and soil foundation characteristics on seepage beneath hydraulic structures. Twelfth International Water Technology Conference, IWTC12 2008, Alexandria, Egypt 1597, 2008.
 Azizi, S., F. Salmasi, A. Abbaspour and H. Arvanaghi, Weep Hole and Cut-off Effect in Decreasing of Uplift Pressure. Journal of Civil Engineering and Urbanism, 2011.
 El-Jumaily, D.K.K. and H.M.J. AL-Bakry Seepage Analysis Through and under Hydraulic Structures Applying Finite Volume Method. Eng. &Tech. Journal, Vol. 31,Part (A), No.9, 2013, 2013.
 Moharrami, A., G. Moradi, M.H. Bonab, J. Katebi and G. Moharrami, Performance of Cutoff Walls Under Hydraulic Structures Against Uplift Pressure and Piping Phenomenon. Geotechnical and Geological Engineering, 2014. 33(1): p. 95-103.
 Krahn, J., Seepage modeling with SEEP/W: An engineering methodology. GEO-SLOPE International Ltd. Calgary, Alberta, Canada, 2004.
 Joorabchi, A., H. Zhang and M. Blumenstein, Application of artificial neural networks to groundwater dynamics in coastal aquifers. Journal of Coastal Research, 2009: p. 966-970.
 Nourani, V., E. Sharghi and M.H. Aminfar, Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran. Artificial Intelligence Research, 2012. 1(2): p. p22.
 Santillán, D., J. Fraile-Ardanuy and M. Toledo. Dam seepage analysis based on artificial neural networks: The hysteresis phenomenon. in Neural Networks (IJCNN), The 2013 International Joint Conference on. 2013. IEEE.
 Al-Suhaili, R.H. and R.A. Karim,
 Mohsin, A.Z., D.H.A. Omran and A.-H.K. Al-Shukur, Dynamic Response of Concrete Gravity Dam on Random Soil. International Journal of Civil Engineering and Technology, 2015. 6(11).
 Yazd, H.G.H., S.J. Arabshahi, M. Tavousi and A. Alvani, Optimal Designing of Concrete Gravity Dam using Particle Swarm Optimization Algorithm (PSO). Indian Journal of Science and Technology, 2015. 8(12): p. 1.
 Al-Delewy, A.A., D.A.-H.K. Shukur and W.H. AL-Musawi, Optimum Design of Control Devices for Safe Seepage under hydaulic structures. Journal of Engineering and Development, Vol. 10, No.1, March (2006) ISSN 1813-7822, 2006.
 Singh, R.M., Optimal hydraulic structures profiles under uncertain seepage head. 2011.
 Singh, R.M. and S. Duggal, Optimal design of hydraulic structures with hybrid differential evolution multiple particle swarm optimization. Canadian Journal of Civil Engineering, 2015. 42(5): p. 303-310.
 Seyedpoor, S., J. Salajegheh, E. Salajegheh and S. Gholizadeh, Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks. Engineering optimization, 2009. 41(5): p. 473-493.
 Hamidian, D. and S. Seyedpoor, Shape optimal design of arch dams using an adaptive neuro-fuzzy inference system and improved particle swarm optimization. Applied Mathematical Modelling, 2010. 34(6): p. 1574-1585.
 Singh, R.M., Design of Barrages with Genetic Algorithm Based Embedded Simulation Optimization Approach. Water Resources Management, 2010. 25(2): p. 409-429.
 Singh, R.M., Genetic algorithm based optimal design of hydraulic structures with uncertainty characterization, in Swarm, Evolutionary, and Memetic Computing. 2011, Springer. p. 742-749.
 Krahn, J., Seepage modeling with SEEP/W: An engineering methodology. GEO-SLOPE International Ltd. Calgary, Alberta, Canada, 2012.
 Lj, T., Dams and appurtenant hydraulic structures. AA BALKEMA Pubi., Taylor & Francis Group pic, 2014: p. 195-196.
 Novak, P., A. Moffat, C. Nalluri and R. Narayanan, Hydraulic structures. 2007: CRC Press.
 Lin, C.D. and B. Tang, Latin hypercubes and space-filling designs. 2015, Book chapter of Handbook of Design and Analysis of Experiments, Bingham, D., Dean, A., Morris, M., and Stufken, J. ed. CRC Press.
 Jain, A. and A. Kumar, An evaluation of artificial neural network technique for the determination of infiltration model parameters. Applied Soft Computing, 2006. 6(3): p. 272-282.
 Sivanandam, S. and S. Deepa, Introduction to neural networks using Matlab 6.0. 2006: Tata McGraw-Hill Education.
 U.S. Army Corps of Engineers, Engineering and Design Flotation Stability Criteria for Concrete Hydraulic Structures. 1987.
 Khosla, A., N.K. Bose and E.M. Taylor, Design of weirs on permeable foundations. 1936.
 Terzaghi, K., R.B. Peck and G. Mesri, Soil mechanics in engineering practice. 1996: John Wiley & Sons.
 US Army Corps Engineers, Gravity Dam Design. Engineer, 1995. 20020626: p. 116.
 Peshko, O., Global Optimization Genetic Algorithms. 2007.