Journal of Hydraulic Structures

Journal of Hydraulic Structures

Development of a new hybrid technique for estimating of relative uplift force in gravity dams based on whale optimization algorithm

Document Type : Research Paper

Authors
Bahram Nourani
Farzin Salmasi
Mohammad Ali Ghorbani
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz-Iran.
10.22055/jhs.2023.41889.1230
Abstract
A numerical model is developed in this study using the finite element method (FEM) to estimate relative total uplift force for different positions of holes of drainage gallery in the foundation of Guangzhao gravity dam, located in China. The data of the relative total uplift force generated for different input combinations using the FEM were used to develop machine learning (ML) models. A three-layer Artificial Neural Network (ANN) and a new hybrid model known as ANN-Whale Optimization Algorithm (ANN-WOA) were used for this purpose. The results showed that R2, RMSE, NSE, KGE and RE% for ANN-WOA model in estimation of the relative total uplift forces were 0.998, 0.021, 0.989, 0.964 and 3.3% respectively and those for ANN model were 0.980, 0.023, 0.982, 0.953 and 4.67% respectively, which indicate the higher accuracy of ANN-WOA model compared to ANN model. The new hybrid model, ANN-WOA with the less RMSE and RE% and high KGE and NSE is a more appropriate model for the estimation of the relative total uplift force. The extracted metrics of violin plots indicated that the probability distribution of the relative total uplift force estimated using ANN-WOA model was very similar to that obtained using the FEM.
Keywords
Gravity dam
Uplift force
Finite element method
Hybrid artificial neural network-whale optimization algorithm
  1. Chen Y, Zhou C, Zheng H (2008). A numerical solution to seepage problems with complex drainage systems. Comput Geotech 35: 383–393. https://doi.org/10.1016/j.compgeo.2007.08.005.
  2. Chawla AS, Thakur RK, Akhleash K (1990). Optimum location of drain in concrete dams. J Energy Eng, ASCE 116(7):930–943. https://doi.org/10.1061/(ASCE)0733-9429(1990)116:7(930).
  3. Salmasi F, Mansuri B, Raoufi A (2015). Use of numerical simulation to measure the effect of relief wells for decreasing uplift in a homogeneous earth dam, Civil Engineering Infrastructures Journal, 48(1): 35-45.https://doi.org/10.7508/CEIJ.2015.01.004.
  4. Nourani B, Salmasi F, Abbaspour A, Oghati B (2016). Numerical investigation of the optimum location for vertical drains in gravity dams, Geotechnical and Geological Engineering, 35(2):799-808. https://doi.org/10.1007/s10706-016-0144-1.
  5. Salmasi F, Khatibi R, Nourani B (2017). Investigating reduction of uplift forces by longitudinal drains with underlined canals, ISH Journal of Hydraulic Engineering, 24(1):81-91. https://doi.org/10.1080/09715010.2017.1350605.
  6. Salmasi F, Nouri M (2017). Effect of upstream semi-impervious blanket of embankment dams on seepage, ISH Journal of Hydraulic Engineering, 25 (2): 143-152, https://doi.org/ 10.1080/09715010.2017.1381862.
  7. Jafari F, Salmasi F, Abraham J (2019). Numerical investigation of granular filter under the bed of a canal, Applied Water Science, 9(137): 1-15, https://doi.org/10.1007/s13201-019-1023-8.
  8. Khosravinia P, Nikpour, MR, Kisi, O, Yaseen, ZM, (2020). Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections. Computers and Electronics in Agriculture 170, 105283.
  9. Khozani ZS, Khosravi K, Pham BT, Kløve B, Mohtar W, Melini WH, Yaseen ZM, (2019). Determination of compound channel apparent shear stress: application of novel data mining models. Journal of Hydroinformatics.
  10. Maroufpoor S, Maroufpoor E, Bozorg-Haddad O, Shiri J, Mundher Yaseen Z (2019). Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. Journal of Hydrology 575, 544–556. doi:10.1016/j.jhydrol.2019.05.045.
  11. Mohammed M, Sharafati A, Al-Ansari N, Yaseen ZM, (2020). Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model. Advances in Civil Engineering 2020.
  12. Sharafati A, Tafarojnoruz A, Shourian, M, Yaseen, ZM (2019b). Simulation of the depth scouring downstream sluice gate: The validation of newly developed data-intelligent models. Journal of Hydro-environment Research.
  13. Daneshfaraz R, Bagherzadeh M, Esmaeeli R; Norouzi R, Abraham J (2021). Study of the Performance of Support Vector Machine for Predicting Vertical Drop Hydraulic Parameters in the Presence of Dual Horizontal Screens, Water Supply (2021) 21 (1): 217–231.https://doi.org/10.2166/ws.2020.279.
  14. Chinh LV, Hiramatsu K, Harada M, Mori M (2009) Estimation of water levels in a main drainage canal in a flat low-lying agricultural area using artificial neural network models.  Water Manage, 96: 1332–1338.  https://doi.org/10.1016/j.agwat.2009.04.005.
  15. Baghalian S, Nazari F (2011). Prediction of uplift pressure under the diversion dam using artificial neural network and genetic algorithm. Int. J. Eng. Appl. Sci. (IJEAS), 3(3): 23–32.
  16. Al-Suhaili RH, Karim RA (2014). Optimal dimensions of small hydraulic structure cutoffs using coupled genetic algorithm and ANN model. J. Eng., 2(20):1-19. https://doi.org/10.13140/RG.2.2.18186.77765.
  17. Nourani V, Hakimzadeh H, Amini AB (2011). Implementation of artificial neural network technique in the simulation of dam breach hydrograph. Journal of Hydroinformatics (2011) 14 (2): 478-496. https://doi.org/10.2166/hydro.2011.114
  18. Khan S, Rana T, Dassanayake D, Abbas A, Blackwell J, Akbar S, Gabriel HF (2009). Spatially distributed assessment of channel seepage using geophysics and artificial intelligence, Irrig. Drain, 58(3):307–320. https://doi.org/10.1002/ird.415.
  19. Sattar AMA (2013). Gene expression models for prediction of dam breach parameters. Journal of Hydroinformatics (2013) 16 (3): 550-571. https://doi.org/10.2166/hydro.2013.084.
  20. Sharghi E, Nourani N, Behfar N (2018). Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. Journal of Hydroinformatics (2018) 20 (5): 1071-1084. https://doi.org/10.2166/hydro.2018.151.
  21. Choi SU, Choi B, Choi S (2015). Improving predictions made by ANN model using data quality assessment: an application to local scour around bridge piers, Journal of Hydroinformatics (2015) 17 (6): 977-989. https://doi.org/10.2166/hydro.2015.097.
  22. Mirjalili S, Lewis A (2016). The Whale Optimization Algorithm. Adv. Eng. Softw, 95, 51–67.
  23. Du P, Wang J, Yang W (2018). Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renew Energy. 122: 533-50. https://doi.org/10.1016/j.renene.2018.01.113.
  24. Yaseen ZM, Ehteram M, Hossain MS, Fai CM, Koting SB, Mohd NS, Jaafar WZB, Afan HA, Hin LS, Zaini N, Ahmed AN, El-Shafie A (2019). A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems. Sustainability (Switzerland). doi:10.3390/su11071953.
  25. Yaseen ZM, Naganna SR, Sa’adi Z, Samui P, Ghorbani MA, Salih, SQ, Shahid S, (2020b). Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms. Water Resources Management 34, 1075–1091. doi:10.1007/s11269-020-02484-w.
  26. Samadianfard S, Hashemi s,  Kargar k, Izadyar M,  Mostafaeipour A,  Mosavi A,  Nabipour N,  Shamshirband Sh (2020). Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm, Energy Reports, (6), 1147-1159, https://doi.org/10.1016/j.egyr.2020.05.001.
  27. Sharafati A, Haghbin M, Motta D, Yaseen, ZM (2019a). The Application of Soft Computing Models and Empirical Formulations for Hydraulic Structure Scouring Depth Simulation: A Comprehensive Review, Assessment and Possible Future Research Direction. Archives of Computational Methods in Engineering 1–25.
  28. Geo-Slope, (2012). Version 8.15.11236, User Manual. GEOSLOPE International, Calgary, in, Alberta, Canada.
  29. Afan, H.A., El-Shafie, A., Yaseen, Z.M., Hameed, M.M., Wan Mohtar, W.H.M., Hussain, A., (2014). ANN Based Sediment Prediction Model Utilizing Different Input Scenarios. Water Resources Management 29, 1231–1245. doi:10.1007/s11269-014-0870-1.
  30. Yaseen ZM, El-Shafie A, Afan HA, Hameed M, Mohtar WHMW, Hussain A (2015). RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Computing and Applications. doi:10.1007/s00521-015-1952-6
  31. Lippmann R (1987). An introduction to computing with neural nets, IEEE ASSP Mag, in, pp. 4-22.
  32. Daneshfaraz R, Ghaderi A, Akhtari A, Di Francesco S (2020). On the Effect of Block Roughness in Ogee Spillways with Flip Buckets. Fluids 2020, 5, 182. https://doi.org/10.3390/fluids5040182.
  33. Yaseen ZM, Awadh, SM, Sharafati A, Shahid S, (2018). Complementary data-intelligence model for river flow simulation. Journal of Hydrology 567, 180–190. doi:10.1016/j.jhydrol.2018.10.020
  34. Yaseen ZM, Faris H, Al-Ansari N (2020a). Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application. Complexity 2020.