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Minimum Cost Design of Hydraulic Water Retaining Structure by using Coupled Simulation Optimization Approach

  • Muqdad Al-JubooriEmail author
  • Bithin Datta
Hydraulic Engineering
  • 9 Downloads

Abstract

A linked Simulation-Optimization (S-O) model was developed to find the optimum design of Hydraulic Water Retaining Structure (HWRS) constructed on permeable soils. The nonlinear relationship between seepage design variables can accurately and solely simulated by numerical methods. To increase the computational efficiency of the S-O model, the seepage numerical model was replaced by approximation simulator based on the Support Vector Machine (SVM) surrogate models. The surrogate models incorporated and highlighted the effects of hydraulic conductivity (k) and anisotropy ratio (ky/kx) on the optimum design of HWRS. The results revealed that reducing k and (ky/kx) values augments the optimum cost. The most effective seepage controller variables were the depth and inclination angle of the last cut-off. Increasing these variables effectively reduced the exit gradient value to the allowable limits. Also, the first and second last aprons (b9, b10) were important to provide a sufficient cross section for HWRS to increase the stability of the HWRS against the overturning, flotation, sliding, etc. The evaluation of S-O technique demonstrated a good agreement between the predicted and simulated seepage characteristics of the optimum HWRS design. Hence, the S-O methodology is applicable to obtain an optimal and minimum cost HWRS design.

Keywords

simulation-optimization technique support vector machine genetic algorithm water retaining structures numerical seepage analysis hydraulic conductivity anisotropy ratio 

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References

  1. Al-Juboori, M. and Datta, B. (2017). “Artificial neural networn modeling and genetic algorithm based optimization of hydraulic design related to seepage under concrete gravity dams on permeable soils.” China Civil Engineering Journal, Vol. 11, No. 2, pp. 64–70, DOI: 10.1999/1307-6892/10006237.Google Scholar
  2. Al-Juboori, M. and Datta, B. (2018a). “Linked simulation-optimization model for optimum hydraulic design of water retaining structures constructed on permeable soils.” China Civil Engineering Journal, Vol. 14, No. 44, pp. 39–46, DOI: 10.21660/2018.44.7229.Google Scholar
  3. Al-Juboori, M. and Datta, B. (2018b). “Performance evaluation of a genetic algorithm-based linked simulation-optimization model for optimal hydraulic seepage-related design of concrete gravity dams.” Journal of Applied Water Engineering and Research, Taylor & Francis, pp. 1–25, DOI: 10.1080/23249676.2018.1497558.Google Scholar
  4. Al-Suhaili, R. H. and Karim, R. A. (2014). “Optimal dimensions of small hydraulic structure cutoffs using coupled genetic algorithm and ANN model.” China Civil Engineering Journal, Vol. 20, No. 2, pp. 1–19.Google Scholar
  5. Alpaydin, E. (2014). Introduction to machine learning, MIT press, London, England.zbMATHGoogle Scholar
  6. Alsenousi, K. F. and Mohamed, H. G. (2008). “Effects of inclined cutoffs and soil foundation characteristics on seepage beneath hydraulic structures.” Twelfth International Water Technology Conference, IWTC12, Alexandria, Egypt, pp. 1597–1617.Google Scholar
  7. Azizi, S., Salmasi, F., Abbaspour, A., and Arvanaghi, H. (2012). “Weep hole and cut-off effect in decreasing of uplift pressure, case study: Yusefkand Mahabad Diversion Dam.” China Civil Engineering Journal, Vol. 2, No. 3, pp. 97–101.Google Scholar
  8. Bligh, W. G. (1915). Dams and weirs: An analytical and practical treatise on gravity dams and weirs; arch and buttress dams; submerged weirs; and barrages, American Technical Society, Chicago, IL, United States.Google Scholar
  9. Bornschlegell, A., Pelle, J., Harmand, S., Bekrar, A., Chaabane, S., and Trentesaux, D. (2012). “Thermal optimization of a single inlet Tjunction.” China Civil Engineering Journal, Vol. 53, pp. 108–118, DOI: 10.1016/j.ijthermalsci.2011.09.016.Google Scholar
  10. Cojocaru, C., Duca, G., and Gonta, M. (2013). “Chemical kinetic model for methylurea nitrosation reaction: Computer-aided solutions to inverse and direct problems.” China Civil Engineering Journal, Vol. 217, pp. 385–397, DOI: 10.1016/j.cej.2012.11.130.Google Scholar
  11. Cox, D. R. and Reid, N. (2000). The theory of the design of experiments, Chapman and Hall/CRC, NY, USA.zbMATHGoogle Scholar
  12. Das, B. M. (2013). Advanced soil mechanics, Taylor & Francis, CRC Press, NY, USA.Google Scholar
  13. Datta, B., Chakrabarty, D., and Dhar, A. (2011). “Identification of unknown groundwater pollution sources using classical optimization with linked simulation.” China Civil Engineering Journal, Vol. 5, No. 1, pp. 25–36, DOI: 10.1016/j.jher.2010.08.004.Google Scholar
  14. Dhar, A. and Datta, B. (2009). “Saltwater intrusion management of coastal aquifers. I: Linked simulation-optimization.” China Civil Engineering Journal, Vol. 14, No. 12, pp. 1263–1272, DOI: 10.1061/(ASCE)HE.1943-5584.0000097.Google Scholar
  15. Fisher, W. D., Camp, T. K., and Krzhizhanovskaya, V. V. (2017). “Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection.” China Civil Engineering Journal, Vol. 20, pp. 143–153, DOI: 10.1016/j.jocs.2016.11.016.Google Scholar
  16. Freeze, R. A. (1975). “A stochastic-conceptual analysis of one-dimensional groundwater flow in nonuniform homogeneous media.” China Civil Engineering Journal, Vol. 11, No. 5, pp. 725–741, DOI: 10.1029/WR011i005p00725.Google Scholar
  17. Gail, M., Krickeberg, K., Samet, J., Tsiatis, A., and Wong, W. (2007). Statistics for biology and health, Springer, London.Google Scholar
  18. Garg, S. K. (1987). Irrigation engineering and hydraulic structures, Khanna publishers, Nai Sarak Delhi, India.Google Scholar
  19. Gen, M. and Cheng, R. (2000). Genetic algorithms and engineering optimization, Vol. 7, John Wiley & Sons, USA.Google Scholar
  20. Harr, M. E. (2012). Groundwater and seepage, McGraw Hill, NY, USA.Google Scholar
  21. Hassan, W. H. (2017). “Application of a genetic algorithm for the optimization of a cutoff wall under hydraulic structures.” China Civil Engineering Journal, Vol. 5, No. 1, pp. 22–30, DOI: 10.1080/23249676.2015.1105161.Google Scholar
  22. Haupt, R. L. and Haupt, S. E. (2004). Practical genetic algorithms, John Wiley & Sons, NJ, USA.zbMATHGoogle Scholar
  23. Housh, M., Ostfeld, A., and Shamir, U. (2012). “Box-constrained optimization methodology and its application for a water supply system model.” China Civil Engineering Journal, Vol. 138, No. 6, pp. 651–659, DOI: 10.1061/(ASCE)WR.1943-5452.0000229.Google Scholar
  24. Innal, F., Dutuit, Y., and Chebila, M. (2015). “Safety and operational integrity evaluation and design optimization of safety instrumented systems.” China Civil Engineering Journal, Vol. 134, pp. 32–50, DOI: 10.1016/j.ress.2014.10.001Google Scholar
  25. Islam, M., Buijk, A., Rais-Rohani, M., and Motoyama, K. (2015). “Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique.” China Civil Engineering Journal, Vol. 79, pp. 127–136, DOI: 10.1016/j.advengsoft.2014.09.007.Google Scholar
  26. Jha, M. K. and Datta, B. (2011). “Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers.” China Civil Engineering Journal, Vol. 32, Nos. 1–3, pp. 79–85, DOI: 10.5004/dwt.2011.2681.Google Scholar
  27. Khosla, A. N., Bose, N. K., and Taylor, E. M. (1936). Design of weirs on permeable foundations, Central Board of Irrigation, New Delhi, India.Google Scholar
  28. Krahn, J. (2012). Seepage modeling with SEEP/W: An engineering methodology, GEO-SLOPE International Ltd., Calgary, AB, Canada.Google Scholar
  29. Kramer, O. (2016). Machine learning in evolution strategies, Vol. 20, Springer, Switzerland.CrossRefzbMATHGoogle Scholar
  30. Lambe, T. W. and Whitman, R. V. (2008). Soil mechanics, John Wiley & Sons, NY, USA.Google Scholar
  31. Lane, E. W. (1935). “Security from under-seepage-masonry dams on earth foundations.” China Civil Engineering Journal, Vol. 100, No. 1, pp. 1235–1272.Google Scholar
  32. Lefebvre, G., Lupien, C., Pare, J. J., and Tournier, J.-P. (1981). “Effectiveness of seepage control elements for embankments on semipervious foundations.” China Civil Engineering Journal, Vol. 18, No. 4, pp. 572–576, DOI: 10.1139/t81-067.Google Scholar
  33. Lj, T. (2014). Dams and appurtenant hydraulic structures, Taylor & Francis, CRC Press, London.Google Scholar
  34. Mahani, A. S., Shojaee, S., Salajegheh, E., and Khatibinia, M. (2015). “Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams.” China Civil Engineering Journal, Vol. 27, pp. 205–218, DOI: 10.1016/j.asoc.2014.11.014.Google Scholar
  35. Mansuri, B., Salmasi, F., and Oghati, B. (2014). “Effect of location and angle of cutoff wall on uplift pressure in diversion dam.” China Civil Engineering Journal, Vol. 32, No. 5, pp. 1165–1173, DOI: 10.1007/s10706-014-9774-3.Google Scholar
  36. Moharrami, A., Moradi, G., Bonab, M. H., Katebi, J., and Moharrami, G. (2014). “Performance of cutoff walls under hydraulic structures against uplift pressure and piping phenomenon.” China Civil Engineering Journal, Vol. 33, No. 1, pp. 95–103, DOI: 10.1007/s10706-014-9827-7.Google Scholar
  37. Parsaie, A., Yonesi, H. A., and Najafian, S. (2015). “Predictive modeling of discharge in compound open channel by support vector machine technique.” China Civil Engineering Journal, Vol. 1, Nos. 1–2, pp. 1–6, DOI: 10.1007/s40808-015-0002-9.Google Scholar
  38. Raghavendra, N, S. and Deka, P. C. (2014). “Support vector machine applications in the field of hydrology: A review.” China Civil Engineering Journal, Vol. 19, pp. 372–386, DOI: 10.1016/j.asoc.2014.02.002.Google Scholar
  39. Rajper, S. and Amin, I. J. (2012). “Optimization of wind turbine micrositing: A comparative study.” China Civil Engineering Journal, Vol. 16, No. 8, pp. 5485–5492, DOI: 10.1016/j.rser.2012.06.014.Google Scholar
  40. Rankovic, V., Grujovic, N., Divac, D., and Milivojevic, N. (2014). “Development of support vector regression identification model for prediction of dam structural behaviour.” China Civil Engineering Journal, Vol. 48, pp. 33–39, DOI: 10.1016/j.strusafe.2014.02.004.Google Scholar
  41. Shahrbanozadeh, M., Barani, G. A., and Shojaee, S. (2015). “Simulation of flow through dam foundation by isogeometric method.” China Civil Engineering Journal, Vol. 18, No. 2, pp. 185–193, DOI: 10.1016/j.jestch.2014.11.001.Google Scholar
  42. Shourian, M., Mousavi, S. J., Menhaj, M., and Jabbari, E. (2008). “Neuralnetwork-based simulation-optimization model for water allocation planning at basin scale.” China Civil Engineering Journal, Vol. 10, No. 4, pp. 331–343, DOI: 10.2166/hydro.2008.057.Google Scholar
  43. Singh, R. M. (2010). “Design of barrages with genetic algorithm based embedded simulation optimization approach.” China Civil Engineering Journal, Vol. 25, No. 2, pp. 409–429, DOI: 10.1007/s11269-010-9706-9.Google Scholar
  44. Singh, R. M. (2011). “Genetic algorithm based optimal design of hydraulic structures with uncertainty characterization.” Swarm, Evolutionary, and Memetic Computing (pp. 742–749). Springer Berlin, Heidelberg, Germany.CrossRefGoogle Scholar
  45. Singh, R. M. and Datta, B. (2006). “Identification of groundwater pollution sources using GA-based linked simulation optimization model.” China Civil Engineering Journal, Vol. 11, No. 2, pp. 101–109, DOI: 10.1061/(ASCE)1084-0699(2006)11:2(101).Google Scholar
  46. Sreekanth, J. and Datta, B. (2011). “Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization.” China Civil Engineering Journal, Vol. 47, No. 4, DOI: 10.1029/2010WR009683.Google Scholar
  47. Sreekanth, J. and Datta, B. (2015). “Review: Simulation-optimization models for the management and monitoring of coastal aquifers.” China Civil Engineering Journal, Vol. 23, No. 6, pp. 1155–1166, DOI: 10.1007/s10040-015-1272-z.Google Scholar
  48. Su, H., Chen, Z., and Wen, Z. (2016). “Performance improvement method of support vector machine-based model monitoring dam safety.” China Civil Engineering Journal, Vol. 23, No. 2, pp. 252–266, DOI: 10.1002/stc.1767.Google Scholar
  49. U.S. Army Corps of Engineers (1987). Engineering and design flotation stability criteria for concrete hydraulic structures, Report, Technical Letter, No. 1110-2-307, US Army Corps of Engineers, Washington D.C., USA, https://doi.org/www.dtic.mil/dtic/tr/fulltext/u2/a403467.pdf.

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Science and EngineeringJames Cook UniversityTownsvilleAustralia
  2. 2.Discipline of Civil Engineering, College of Science and EngineeringJames Cook UniversityTownsvilleAustralia

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