Dam Seepage Prediction Using RBF and GFF Models of Artificial Neural Network; Case Study: Boukan Shahid Kazemi's Dam

Document Type: Research Note

Authors

1 Tabriz university

2 Water Engineering Department of Gorgan

3 Water Engineering Department of Tabriz

Abstract

Dams have been always considered as the important infrastructures and their critical values measured. Hence, evaluation and avoidance of dams’ destruction have a specific importance. In this study seepage of the embankmentof Boukan Shahid Kazemi’s dam in Iran has been analyzed via RBF (radial basis function network) and GFF (Feed-Forward neural networks) models of Artificial Neural Network (ANN). RBF and GFF of ANN models were trained and verified using each piezometer’s data and the water levels difference of the dam. To achieve this goal,based on the number of data and inputs,864piezometric data set were used, of which 80% (691 data) was used for the training and 20% (174 data) for the testing the network.The results showed good agreement between observed and predicted values and concluded the RBF model has high potential in estimating seepage with Levenberg Marquardt training and 4 hidden layers. Also the values of statistical parameters R2 and RMSE were 0.81 and the 33.12.

Keywords

Main Subjects


[1] Jafari, M. H. (2014). Prediction seepage of dam embankment using data analysis methods. Civil engineering master science thesis, Islamic Azad University of Semnan. (In Persian).

[2] Ersayin, D. (2006). Studying seepage in a body of earth-fill dam by (Artificial Neural Networks. Doctoral dissertation, Master Thesis, İzmir Institute of Technology.

[3] Nourani, V., Sharghi, E. Aminfar, M. H. (2012). Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran. Artificial Intelligence Research, 1(2): 22 pages.

[4] Poorkarimi, S., Maghsoodian, S. MolaAbasi, H. Kordnaeij, A. (2013). Seepage evaluation of an earth dam using Group Method of Data Handling (GMDH) type neural network: A case study. Scientific Research and Essays Journal, 8(3): 120-127.

[5] Kamanbedast, A., Delvari, A. (2013). Analysis of Earth Dam: Seepage and Stability Using Ansys and Geo-Studio Software. World Applied Sciences Journal, 17(9): 1087-1094.

[6] Ebtehaj, I., Bonakdari, H. (2013). Evaluation of Sediment Transport in Sewer using Artificial Neural Network. Engineering Applications of Computational Fluid Mechanics, 7(3): 382-392.

[7] Aljairry, H. (2010). 2D-Flow Analysis through Zoned Earth Dam Using Finite Element Approach. Engineering and Technology Journal, pp: 21.

[8] Shamsaie, A. (2004). Design and construction of reservoir dams (Embankment gravel dams). Science and Technology University. (In Persian)

[9] Nourani, V., Babakhani, A. (2012). Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modeling.  Journal of Computing in Civil Engineering, Published online, doi: http: //dx.doi.org/ 10.1061/ (ASCE) CP. 1943-5487.0000200.

[10] Naderpour,H., Fakharian, P., Rafiean, A. H. Yourtchi, E. (2016). Estimation of the Shear Strength Capacity of Masonry Walls Improved with Fiber Reinforced Mortars (FRM) Using ANN-GMDH Approach. Journal of Concrete Structure and Materials, 2: 47-59. (In Persian)

[11] Jamal, A., Nikoo, M. R. Karimi, A. 2011. Estimition of the amount of seepage to the clay core of damsin unstable conditions by using artificial neural network. The first conference of computer systems intelligence. Payame noor University of Tehran.

[12] Santillán, D., Fraile-Ardanuy, J. Toledo, M. A. (2013). Dam seepage analysis based on artificial neural networks: The hysteresis phenomenon. International Joint Conference on Neural Networks (IJCNN).

[13] Naderpour, H., Fakharian, P. (2017). Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks, Journal of Structural and Construction Engineering, doi: 10.22065/jsce.2017.70668.1023.

[14] Ebtehaj, I., Bonakdari, H. Zaji, A. H. (2016). An expert system with radial basis function neural network based on decision trees for predicting sediment transport in sewers. Water Science and Technology, 74(1): 176-183. doi:10.2166/wst.2016.174.

[15] Naderpour, H., Rafiean, A. H. Fakharian, P. (2018). Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16: 213-219, https://doi.org/10.1016/j.jobe.2018.01.007.

[16] www.agrw.ir/

[17] Alborzi, M. Know with artificial neural networks. Amirkabir University Press. (In Persian).

[18] Shirzad, M., Nashaie, M. A. Mohammadi, K. (2006). Use of artificial neural networks to estimate sediment on Sepidrood dam. Irrigation and Drainage Networks, 12-14 May, Shahid chamran Ahvaz University, Faculty of Water Engineering. (In Persian)

[19] Kohonen, T. (1998). An introduction to neural computing, presented at the Proc. IEEE First Int. Conf. on Neural Networks.