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IJSTR >> Volume 9 - Issue 8, August 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

The Influence Of Hydraulic Parameters In Different Groundwater Systems On The Results Of Modular Groundwater Optimizer (MGO)

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Confined and Semiconfined Aquifers, Dewatering systems, Groundwater system, Hydraulic Parameters, Modular Groundwater Optimizer (MGO), Visual Modflow.



The aim of this study is to evaluate the effect of the hydraulic parameters of groundwater systems on the results of simulation optimization modelling when applied to the dewatering systems design for different construction sites in Egypt. The hydraulic parameter which will be evaluated is mainly the hydraulic conductivity represented by the position of the groundwater table according to different soil stratification taking into account the different excavation depths at the construction sites. This work takes into consideration six executed construction projects, classified into two groups according to the position of the groundwater table with respect to the depth of excavation; the first group where the excavation reaches Sandy Soil (ESS), the second group where excavation reaches Clayey Soil (ECS) the two systems are treated as semi-confined and confined systems respectively. The Modflow as a numerical simulation model and the Modular Groundwater Optimizer (MGO) as an optimization model were integrated with each other as a simulation-optimization tool. Each group or system was simulated by the model using the pumping test results obtained from the field data and the wells which were already executed for pumping. The model was run until reaching the values of the drawdown that were observed by the piezometric/observed wells at each site. The model was run another time using MGO in order to minimize the wells number, the quantity of water pumped from them and the dewatering systems cost. By comparing the outputs of the two runs for the same site regarding the achieved drawdown value either by the executed wells or from the optimization results of the two groups of ESS and ECS, the comparison revealed that the drawdown can be achieved with average saving of (28%, and 25%) of the actually number of executed wells respectively, so it is highly recommended to apply MGO when designing any dewatering system in order to achieve the most cost-effective pumping system especially in the case of ESS.



[1] Abbas, S. A. (2016). Optimum Management Of Groundwater Pumping By Using Simulated Annealing Technique. Kufa Journal of Engineering, 7(2), 9-19.
[2] Bear, J. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (Vol. viii). Oxford, UK: McGraw-Hill, New York Holland, J.H. ; U Michigan Press.
[3] Cooper , H. H., & Jacob, C. E. (1946). “A generalized graphical method for evaluating formation constants and summarizing well field history. Am. Geophysics. Union Trans., 27, 526-534.
[4] HUANG, Shan-Shan; et. al. (2018). Foundation pit dewatering optimization design based on GMW-2005 and LGR technique. Journal of Groundwater Science and Engineering., 6 ( 3), 234-242. doi:10.19637/j.cnki.2305-7068.2018.03.008
[5] Javad, D. S., & Mojtaba, S. (2015). Optimum Dewatering Network Design Using Firefly Optimization Algorithm. World Academy of Science, Engineering and Technology Environmental and Ecological Engineering, 2(3).
[6] Jiang S., K. X. (2013). Groundwater dewatering optimization in the Shengli No. 1 open-pit coalmine, Inner Mongolia, China. Environmental Earth Sciences, 69(1), 187-196.
[7] Mansour, M. A., & Aly, M. M. (2020). A simulation-optimization approach for the optimal design of dewatering systems in unconfined strata. Alexandria Engineering Journal, 59(2), 839-850. doi:10.1016/j.aej.2020.02.029
[8] Mansour, M. A., Samieh, A. M., Radwan, A. M., & Ahmed, E. A. (2019, December). Responses of Groundwater Lowering Systems: Empirical Equations Via Field Records. International Journal of Engineering and Advanced Technology (IJEAT), 9(2), 1527-1535. doi:10.35940/ijeat.B3658.129219
[9] Michalewicz, Z. (1992). Data Structures + Genetic Operators = Evolution Programs (Vol. Symbolic Computation Series). Springer-Verlag.
[10] Powers, J., Corwin, A., Schmall , P. C., & Kaeck, W. E. (2007). Construction Dewatering and Groundwater Control: New Methods and Applications (Vol. Third Edition. ). John Wiley & Sons, Inc. doi:ISBN: 978-0-471-47943-7
[11] Preene, M., Roberts, T., Powrie, W., & Dyer, M. (2000). Groundwater Control – Design and Practice. C115. CIRIA.
[12] Wang, C., & Zheng, M. (1998). Groundwater management optimization using Genetic Algorithms and Simulated Annealing: Formulation and Comparison. Journal of the American Water Resources Association (JAWRA)., 34(3).
[13] Yi Liu et. al. (2019). A New Optimization Method for the Layout of Pumping Wells in Oases: Application in the Qira Oasis, Northwest China. Water, 11(5, 970; ). doi:10.3390/w11050970
[14] Zheng , C., & Wang, P. (2003). MGO Modular Groundwater Optimizer Incorporating MODFLOW/MT3DMS: Documentation and User’s Guide,. The University of Alabama, in cooperation with Groundwater Systems Research Ltd.