Predesign of an Urban Rainfall Drainage Network with Genetic Algorithms
Journal: Journal of Building Technology DOI: 10.32629/jbt.v6i2.2475
Abstract
The pre-design of an urban storm drainage network is proposed with an optimization method consisting of two parts: in one part, the connection of the manholes is proposed so that there is little water flow in the pipes; in the other part, it is based on a genetic algorithm to find the lowest cost of acquisition of the pipes and of the excavation necessary for their installation, complying with several hydraulic and constructive restrictions recommended in construction manuals of networks of this type. The described procedure is made from the linking of the pipes that make up the network (network layout) trying to obtain the smallest sum of the lengths. It is considered that the conduits are of circular section, where their diameters only take commercial values. The slope of each pipe in the network is calculated so that the hydraulic depth of the water flow is 80% of the pipe diameter, in order to approximate the flow conduction capacity of the liquid within it. Finally, its application to a real case is demonstrated, and the hydraulic performance of the pre-designed network is evaluated by simulating the non-permanent free surface flow in the network using the EPA-SWMM 5.1.015 model, in order to ensure that the hydraulic restrictions and assumptions made for permanent flow are met.
Keywords
design; drainage network; rainwater; sewerage; genetic algorithm; non-permanent flow in free surface
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[4] Fuentes-Mariles, O. A., Palma-Nava, A., & Rodríguez-Vázquez, K. 2011. Estimación y localización de fugas en una red de tuberías de agua potable usando algoritmos genéticos. Ingeniería Investigación y Tecnología, 12(02), 235-242. https://doi.org/10.22201/fi.25940732e.2011.12n2.023
[5] Hernández, A. D. A. 2007. Un método de diseño de redes de alcantarillado. (Tesis de maestría). Universidad Nacional Autónoma de México.
[6] Manual de Agua Potable, Alcantarillado y Saneamiento (MAPAS). 2019. Secretaría del Medio Ambiente y Recursos Naturales, Comisión Nacional del Agua, México.
[7] Mockus, V. 1957. Use of storm and watershed characteristics in synthetic hydrograph analysis and application. Department of Agriculture, Soil Conservation Service, USA.
[8] Reich, B. M. 1963. Short-duration rainfall-intensity estimates and other design aids for regions of sparse data. Journal of Hydrology, 1(1), 3-28. https://doi.org/10.1016/0022-1694(63)90029-5
[9] Rossman, L. A. 2005. Storm water management model user's manual. Version 5.0., National.
[10] Risk Management Research Laboratory Office of Research and Development USA. Environmental Protection Agency, Cincinnati, OH 45268.
[11] Salas, J. D., & Obeysekera, J. M. 2014. Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. Journal of Hydrologic Engineering, 19(3). https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820.
[12] Chompa, A. J. A. 2019. Diseño de una red de drenaje pluvial mediante un algoritmo de optimización y la revisión de su funcionamiento hidráulico. (Tesis de maestría). Universidad Nacional Autónoma de México.
[13] Coley, A. D. 1999. An introduction to genetic algorithms for scientists and engineers. USA: World Scientific.
[14] Dandy-Graeme, C., Simpson-Angus, R., & Murphy-Laurence, J. 1996. An improved genetic algorithm for pipe network optimization. Water Resources Research, 32(2), 449-458.
[15] Escalante-Sandoval, C., & Reyes-Chávez, L. 2002. Técnicas estadísticas en hidrología. México: Facultad de Ingeniería, Universidad Nacional Autónoma de México.
[16] Goldberg, D. E. 1999. Genetic algorithms in search, optimization, and machine learning. USA: Addison Wesley.
[17] Sotelo, A. G. 2002. Hidráulica de canales. México: Facultad de Ingeniería, Universidad Nacional Autónoma de México.
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