Design of Gravity Dam by Genetic Algorithms

Authors: Farzin Salmasi

Abstract:

The design of a gravity dam is performed through an interactive process involving a preliminary layout of the structure followed by a stability and stress analysis. This study presents a method to define the optimal top width of gravity dam with genetic algorithm. To solve the optimization task (minimize the cost of the dam), an optimization routine based on genetic algorithms (GAs) was implemented into an Excel spreadsheet. It was found to perform well and GA parameters were optimized in a parametric study. Using the parameters found in the parametric study, the top width of gravity dam optimization was performed and compared to a gradient-based optimization method (classic method). The accuracy of the results was within close proximity. In optimum dam cross section, the ratio of is dam base to dam height is almost equal to 0.85, and ratio of dam top width to dam height is almost equal to 0.13. The computerized methodology may provide the help for computation of the optimal top width for a wide range of height of a gravity dam.

Keywords: Chromosomes, dam, genetic algorithm, globaloptimum, preliminary layout, stress analysis, theoretical profile.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062076

References:

[1] U.S. Army Corps of Engineers, "Gravity Dam Design". EM 1110-2- 2200. 1995.
[2] U.S.B.R., "Design of Gravity Dams". Design Manual for Concrete Gravity Dams, U.S. Government Printing Office. 1976.
[3] S. L., Atmapoojya, Mahajan, S.K., and Dabhade, A.N., "Computation of optimal top width of gravity dam", Department of Civil Engineering, Kavikuluguru Institute of Technology and Science, Ramtek. District, Nagpur (MS), India. Taylor and Francis Group plc, London, UK. 2004.
[4] W. P. Creager, "The economical top width of non overflow dams". ASCE, 80: 723, 1916.
[5] M. Sarabian, and L . V. Lee, "A Modified Partially Mapped MultiCrossover Genetic Algorithm for Two-Dimensional Bin Packing Problem". Journal of Mathematics and Statistics, 6: 157-162. DOI: 10.3844/jmssp.2010.157.162.
[6] A. Rayner, "Summarizing Relational Data Using Semi-Supervised Genetic Algorithm-Based Clustering Techniques". 2010, Journal of Computer Science, 6: 775-784. DOI: 10.3844/jcssp.2010.775.784.
[7] G. Al Rahedi and J. Atoum, "Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms". 2009, American Journal of Applied Sciences, 6: 1586-1590. DOI: 10.3844/ajassp.2009.1586.1590.
[8] T. D. Asfaw, and S. Saiedi, "Optimal short-term cascade reservoirs operation using genetic algorithm". 2011, Asian J. Applied Sci., 4: 297- 305.
[9] D. R. Kumar, and B. Vidivelli, "Acrylic Rubber Latex in Ferro cement for Strengthening Reinforced Concrete Beams". 2010, American Journal of Engineering and Applied Sciences, 3: 277-285. DOI: 10.3844/ajeassp.2010.277.285.
[10] Samuel M. Tessa, "Temperature Optimization for Bioethanol Production from Corn Cobs Using Mixed Yeast Strains". 2010, Online Journal of Biological Sciences, 10: 103-108. DOI: 10.3844/ojbsci.2010.103.108.
[11] Khalid A. Eldrandaly, "Integrating Gene Expression Programming and Geographic Information Systems for Solving a Multi Site Land Use Allocation Problem". American Journal of Applied Sciences, 2009, 6: 1021-1027. DOI: 10.3844/ajassp.1021.1027.
[12] B. K. Haut Bugang, H. Ali Faisal and R. S. K. Rajoo, "Stability Analysis and Stability Chart for Unsaturated Residual Soil Slope". American Journal of Environmental Sciences, 2006, 2: 154-160. DOI: 10.3844/ajessp.,154.160.
[13] D. Yedjour, H. Yedjour and A. Benyettou, "Combining quine mccluskey and genetic algorithms for extracting rules from trained neural networks". Asian J. Applied Sci., 2010, 4: 72-80.
[14] D., Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning". Addison-Wesley. 1989.