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Prediction of Crest Settlement of Center Cored Rockfill Dam using an Artificial Neural Network Model

인공신경망기법을 이용한 중심차수벽형 석괴댐의 정부침하량 예측

  • 김용성 (강원대학교 농업생명과학대학 지역건설공학과) ;
  • 김범주 (동국대학교 공과대학 건설환경공학과) ;
  • 오상은 (강원대학교 농업생명과학대학 바이오자원환경학과)
  • Received : 2012.05.14
  • Accepted : 2012.06.13
  • Published : 2012.07.31

Abstract

In this study, the settlement data of 32 center cored rockfill dams (total 39 monitored data) were collected and analyzed to develop the method to predict the crest settlement of a CCRD after impounding by using the internal settlement data occurred during construction. An artificial neural network (ANN) modeling was used in developing the method, which was considered to be a more reliable approach since in the ANN model dam height, core width, and core type were all considered as input variables in deriving the crest settlement, whereas in conventional methods, such as Clements's method, only dam height is used as a variable. The ANN analysis results showed a good agreement with the measured data, compared to those by the conventional methods using regression analysis. In addition, a simple procedure to use the ANN model for engineers in practice was provided by proposing the equations used for given input values.

Keywords

References

  1. Clements, R. P., 1984. Post-Construction Deformation of Rockfill Dams. Journal of Geotechnical Engineering 110(7): 821-837. https://doi.org/10.1061/(ASCE)0733-9410(1984)110:7(821)
  2. Dascal, O., 1987. Postconstruction Deformations of Rockfill Dams. Journal of Geotechnical Engineering 113(1): 46-59. https://doi.org/10.1061/(ASCE)0733-9410(1987)113:1(46)
  3. Habibagahi, G., 2002. Post-construction Settlement of Rockfill Dams Analyzed via Adaptive Network-based Fuzzy Inference Systems. Computers and Geotechnics 29: 211-233. https://doi.org/10.1016/S0266-352X(01)00025-8
  4. Hunter, G., 2003. The Pre- and Post-failure Deformation Behavior of Soils Slopes. Ph.D. Thesis, Univ. of New South Wales, New South Wales, Australia.
  5. Kim, Y. S. and B. T. Kim, 2008. Prediction of Relative Crest Settlement of Concrete-faced Rockfill Dams Analyzed using an Artificial Neural Network Model. Computers and Geotechnics 35: 313-322. https://doi.org/10.1016/j.compgeo.2007.09.006
  6. K-water, 2005. Dam Intergration Information System. Technical report. Korea Water Resources Corporation, Korea (in Korean).
  7. Lawton, F. L. and M. D. Lester, 1964. Settlement of Rockfill Dams. Proceedings, Eighth International Congress on Large Dam. Q.32 R.2. Edinburgh, Scotland 3: 599-613.
  8. Shin, H., 2001. Neural Network Based Constitutive Models For Finite Element Analysis. Ph.D Thesis, University of Wales, Swansea, United Kingdom.
  9. Soydemir, C. and B. Kjaernsli, 1979. Deformation of Membraine-faced Rockfill Dams. Seventh European Conference on Soil Mechanics and Foundation Engineering 3: 281-284.
  10. Sowers, G. F., R. C. Williams and T. S. Wallace, 1965. Compressibility of Broken and the Settlement of Rockfills. Proceedings, Sixth International Conference on Soil Mechanics and Foundation Engineering. Toronto, Canada 2: 561-565.

Cited by

  1. Long-term Settlement Prediction of Center-cored Rockfill Dam using Measured Data vol.15, pp.11, 2014, https://doi.org/10.14481/jkges.2014.15.11.21
  2. A Study on Settlement Prediction of Concrete-faced Rockfill Dam Using Measured Data During Construction and After Impounding vol.16, pp.2, 2015, https://doi.org/10.14481/jkges.2015.16.2.5