Advertisement

Indian Geotechnical Journal

, Volume 49, Issue 2, pp 232–240 | Cite as

An Investigation of PSO Algorithm-Based Back Analysis on the Three-Dimensional Seepage Characteristics of an Earth Dam

  • Shoukai Chen
  • Xinfei LiuEmail author
Technical Note
  • 73 Downloads

Abstract

In view of the problem of seepage safety and the character of dangerous reservoir earth dam, the 3D seepage of FEM back analysis was studied. Based on a reservoir project, the rationality of prototype observation data in recent years is analyzed, and the effective monitoring point and its observation data are selected for back analysis. The simulation calculation model is built, and the permeability coefficient of key materials and seepage characteristics of calculation area of earth dam are obtained after using particle swarm optimization (PSO) algorithm as the inversion algorithm and using FEM as the seepage field algorithm method of improved nodal virtual flux. The results shows that the PSO, which has high efficiency with back analyzing the permeability coefficient of materials in the dam and satisfactory accuracy, can meet the requirements of engineering, and a large seepage gradient in overflow point and boundary of earth dam, which is unfavorable for dam seepage stability, is produced when the problem of contact erosion is also existed in connection part of earth dam section and masonry dam section.

Keywords

Reservoir earth dam Seepage Back analysis PSO algorithm Permeability coefficient 

Notes

Acknowledgements

This work was supported by The National Natural Science Foundation of China (Grant Nos. 51309101, 51679092), Henan Province Major Scientific and Technological Projects of China (Grant No. 172102210372), and Henan Province Cooperation of Production, Teaching and Research of China (Grant No. 182107000031).

References

  1. 1.
    Niu XQ (2010) Characteristics of reservoir defects and rehabilitation technology in China. Chin J Geotech Eng 32(1):153–157Google Scholar
  2. 2.
    Niu WJ, Ye WM (2007) The finite element method for the free surface of the seepage in homogenous earth dam on imperviousness groundwork. Rock Soil Mech 28(s1):375–378Google Scholar
  3. 3.
    Chi S, Ni S, Liu Z (2015) Back analysis of the permeability coefficient of a high core rockfill dam based on a RBF neural network optimized using the PSO algorithm. Math Probl Eng 118:1–15Google Scholar
  4. 4.
    Sheng JB, Liu JX, Zhang SC (2008) Investigation and analysis of failure mechanism of reinforced dams. Chin J Geotech Eng 30(11):1620–1625Google Scholar
  5. 5.
    Zhao JC, Wang CF, Jiang LH (2005) Study on eliminating danger and reinforcing of old hazardous middle–small reservoirs. J Chin Three Gorges Univ (Nat Sci) 27(2):119–122Google Scholar
  6. 6.
    Himanshu N, Burman A (2017) Seepage and stability analysis of durgawati earthen dam: a case study. Indian Geotech J 2017(1):1–20Google Scholar
  7. 7.
    Hu Q, She CX (2004) Seepage and safety analysis as well as reinforcement of soil dam of Xianling reservoir. Rock Soil Mech 25(3):503–506Google Scholar
  8. 8.
    Ahmad A, El-Shafie A, Razali SFM (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28(11):3391–3405Google Scholar
  9. 9.
    Esmaeilzadeh M, Talkhablou M, Ganjalipour K (2017) Arching parametric study on earth dams by numerical modeling: a case study on darian dam. Indian Geotech J 6:1–18Google Scholar
  10. 10.
    Wang L (2001) Intelligent optimization algorithms and its applications. Press of Tsinghua University, BeijingGoogle Scholar
  11. 11.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of IEEE International Conference On Neural Networks, Perth, WA, pp 1942–1948.  https://doi.org/10.1109/icnn.1995.488968
  12. 12.
    Cui HD, Zhu YM (2009) Improved procedure of nodal virtual flux of global iteration to solve seepage free surface. J Wuhan Univ Technol (Transp Sci Eng) 33(2):238–241Google Scholar
  13. 13.
    Chen SK, Yan J, Li JM (2011) Seepage field 3D finite element simulation of concrete faced rockfill dam under failure condition of vertical fracture. Rock Soil Mech 32(11):3473–3478Google Scholar
  14. 14.
    Nasimi R, Shahbazian M, Irani R (2011) Permeability estimation of the reservoir based on particle swarm optimization coupled with artificial neural networks. Pet Sci Technol 29(22):2329–2337Google Scholar
  15. 15.
    Shourian M, Mousavi SJ, Tahershamsi A (2008) Basin-wide water resources planning by integrating PSO algorithm and modsim. Water Resour Manag 22(10):1347–1366Google Scholar
  16. 16.
    Nasimi R, Irani R (2015) Combining a neural network with a genetic algorithm and particle swarm optimization for permeability estimation of the reservoir. Energy Sources 37(4):384–391Google Scholar
  17. 17.
    Irani R, Nasimi R (2011) Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir. Expert Syst Appl 38(8):9862–9866Google Scholar
  18. 18.
    Stojanovic B, Milivojevic M, Ivanovic M (2013) Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv Eng Softw 65(10):182–190Google Scholar
  19. 19.
    Wang L, Zeng J, Xu L (2011) A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization. Inf Technol Manag 12(2):111Google Scholar
  20. 20.
    Shi YH, Eberhart R (1998) A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, AK, pp 69–73.  https://doi.org/10.1109/ICEC.1998.699146
  21. 21.
    Chen SK, Zhang XY (2016) Seepage control in a high concrete face-rock fill dam based on the node virtual flow method. Open Constr Build Technol J 12(11):547–560Google Scholar
  22. 22.
    Li PC, Kong XY, Lu DT (2003) Mathematic model of fluid–solid coupling seepage in saturated porous media. Adv Hydrodyn 18(4):419–426Google Scholar
  23. 23.
    Wei JS, Shen ZZ, Wu LC (2011) Automatic generation and application of contact elements in 3D finite element model of concrete face rockfill dam. Power Hydropower Eng 29(12):67–69Google Scholar
  24. 24.
    Cui Y, Huang T, Peng D (2014) Simulation of seepage field and calculation of leakage in reservoir dam site of western sichuan plateau. J Water Res Water Eng 25(05):51–54Google Scholar
  25. 25.
    Cen WJ, Ren XH, LI QS (2007) Stress and deformation analysis of high CFRD under complicated topographic condition. J Hohai Univ 06(04):452–455Google Scholar
  26. 26.
    Chen SK, Zhang X (2016) Seepage control in a high concrete face-rock fill dam based on the node virtual flow method. Open Constr Build Technol J 12(10):547–560Google Scholar
  27. 27.
    Wang XJ, Pan JS (2010) Study on position temperature of multi-concentrated leaking channel in dike. Chin J Geotech Eng 32(11):1800–1805Google Scholar

Copyright information

© Indian Geotechnical Society 2018

Authors and Affiliations

  1. 1.School of Water ConservancyNorth China University of Water Resources and Electric PowerZhengzhouChina
  2. 2.Henan Key Laboratory of Water Environment Simulation and TreatmentZhengzhouChina
  3. 3.Henan Water Environment Management and Ecological Rehabilitation Academician WorkstationZhengzhouChina
  4. 4.Collaborative Innovation Center of Water Resources Efficient Utilization and Protection EngineeringZhengzhouChina

Personalised recommendations