Prediction models for estimation of exit hydraulic gradient and uplift pressure under the influence of downstream filter

Document Type : Research Article

Authors

1 tabriz university

2 Tabriz University

Abstract

This study investigates the impact of filter which is located in downstream of the hydraulic structures for reduction of uplift pressure and hydraulic gradient. Effective parameters for design of filter are: length of filter (L), distance from downstream of structure (X) and upstream water head (H). The outcomes of this study showed that design of filter with L/H equal to 0.057, results 60% reduction of downstream uplift pressure and 10% reduction of upstream uplift pressure. Thus the effect of filter in uplift pressure in downstream of floor is impressive. By increasing the filter length, exit hydraulic gradient always decreases and with increasing the distance of downstream (X), the effect of filter in reduction exit gradient increases. Design of filter with L/H equal to 0.057 have a good impact on exit hydraulic gradient reduction (65%), witch by increasing the length of filter, hydraulic gradient reduction will be reduced. Finally, regression and artificial intelligence models (RBF, MLP and SVM) were used for prediction of uplift pressure and exit hydraulic gradient in structure with filter. Comparison of these models base on two error measurements (R2, RMSE and MAE) demonstrated that regression model is a suitable model and SVM as a poor model in prediction of uplift pressure and hydraulic gradient.

Keywords

Main Subjects


[1] R. Hasan, Embankment Dams, 2004.
[2] H. Khalili Shayan, E. Amiri-Tokaldany, Effects of blanket, drains, and cutoff wall on reducing uplift pressure, seepage, and exit gradient under hydraulic structures, International Journal of Civil Engineering,13(4)(2015)486-500.
[3] S. Mcloughlin, A. Ahmed, Seepage under Hydraulic Structures Provided with an Intermediate Filter, in, .2102
[4] M. Farouk, I. Smith, Design of hydraulic structures with two intermediate filters, Applied Mathematical Modelling, 24 (2000) 779-794.
[5] A. Kumar, B. Singh, A.S. Chawla, Design of Structures With Intermediate Filters, Journal of Hydraulic Engineering, 112(3) (1986) 206-219.
[6] W.H. AL-Musawi, A.-H.K. Shukur, A.A.A. Al-Delewy, Optimum Design of Control Devices for Safe Seepage under Hydraulic Structures, in, 2006.
[7] F. Salmasi, M. Nouri, Effect of upstream semiimpervious blanket of embankment dams on seepage, ISH Journal of Hydraulic Engineering, 25(2) (2019) 143-152.
[8] D. Petković, M. Gocic, S. Trajkovic, S. Shamshirband, S. Motamedi, R. Hashim, H. Bonakdari, Determination of the most influential weather parameters on reference evapotranspiration by adaptive neurofuzzy methodology, Computers and Electronics in Agriculture, 114 (2015) 277-284.
[9] G. Tayfur, D. Swiatek, A. Wita, V.P. Singh, Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland, Journal of Hydraulic Engineering, 131(6) (2005) 431-440.
[10] A.M.A. Sattar, Gene expression models for prediction of dam breach parameters, Journal of Hydroinformatics, 16(3) (2013) 550-571.
[11] D.N. Ural, M. Tolon, Slope Stability during Earthquakes: A Neural Network Application, in: GeoCongress 2008, 2008, pp. 878-885.
[12] SEEP/W. Seepage Modeling with SEEP/W. GeoSlope International Ltd, Calgary, 2012.
[13] H, Khalili Shayan. E.  Amiri Tokaldany, experimental and Numerical investigation of bligh and lane creep theorem in prediction of diversion dams uplift, 10th Iranian hydraulic conference, 2011.
[14] USBR, Embankment dams, Department of interior bureau of reclamation, 2014.
[15] S. Haykin, Neural Networks, A Comprehensive Foundation, Prentice-Hall Inc.NJ, 1999.
[16] V.P. N. Dehghani, S. Khanmohammadi, assessment of MLP and RBF model in prediction of Monthly evaporation, in:  2th National Conference on Sustainable development Agriculture and Healthy Environment, Iran, 2013.
[17] Y.B. Dibike, D.P. Solomatine, River flow forecasting using artificial neural networks, Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(1) (2001) 1-7.
[18] T. Kavzoglu, I. Colkesen, A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 11(5) (2009) 352359.
[19] B. Guo, S.R. Gunn, R.I. Damper, J.D.B. Nelson, Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification, IEEE Transactions on Image Processing, 17(4) (2008) 622-629.