Abstract
Unlike calculation of scour depth around of a single pier, estimation of scour depth around group piers is a complex problem. In this research, four types of artificial neural networks (ANNs) were applied multi layer perceptron (MLP), radial basis function (RBF), neuro fuzzy inference system (ANFIS), and support vector machine (SVM). The inputs of ANNs were coordinates of considered points in the channel (X, Y), size of pier, flow depth, flow discharge, flow velocity, and critical velocity for inception of sediment transport. The trained methods were momentum and Levenberg–Marquardt (LM) training methods. Also, genetic algorithm (GA) was applied for optimization of MLP and RBF. Two group piers were used (three circular piers and three square piers) in a channel with stable flow conditions, scouring of clear water, and uniform bed particles (with D50 = 1.31 mm). Outputs of different ANNs (scour depth) were compared with observed data using of performance criteria correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE). For circular piers, between square piers and behind of gear square pier, the best ANN is RBF-GA, while in front of first square pier, the best ANN is MLP-GA. The best ANNs have two hidden layers, and their training method is the Levenberg–Marquardt (LM). The values of R, MSE, and MAE of RBF-GA are 0.997, 1.66 mm2, and 0.9 mm, and the values of R, MSE, and MAE of MLP-GA are 0.997, 2.55 mm2, and 1.16 mm. Also, the sensitive analysis shows that threshold velocity (Uc) is the most effective factor on scour depth
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References
Ahmadianfar I, Jamei M, Chu X (2019) Prediction of local scour around circular piles under waves using a novel artificial intelligence approach. Mar Georesour Geotechnol:1–12. https://doi.org/10.1080/1064119X.2019.1676335
Amini A, Melville BW, Ali TM, Ghazali AH (2012) Clear-water local scour around pile groups in shallow-water flow. J Hydraul Eng ASCE 138(2):177–185. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000488
Amini A, Melville BW, Ali TM (2014) Local scour at piled bridge piers including an examination of the superposition method. Can J Civ Eng 41(5):461–471. https://doi.org/10.1139/cjce-2011-0389
Ataie-Ashtiani B, Beheshti A (2006) Experimental investigation of clear-water local scour at pile groups. J Hydraul Eng ASCE 132(10):1100–1104. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:10(1100)
Azimi H, Bonakdari H, Ebtehaj I, Talesh SHA, Michelson DG, Jamali A (2017) Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets Syst 319:50–69. https://doi.org/10.1016/j.fss.2016.10.010
Baghbadorani DA, Ataie-Ashtiani B, Beheshti A, Hadjzaman M, Jamali M (2018) Prediction of current-induced local scour around complex piers: review, revisit, and integration. Coast Eng 133:43–58. https://doi.org/10.1016/j.coastaleng.2017.12.006
Bateni SM, Vosoughifar HR, Truce B, Jeng DS (2019) Estimation of clear-water local scour at pile groups using genetic expression programming and multivariate adaptive regression splines. J Waterw Port C-ASCE 145(1):04018029. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000488
Chou JS, Pham AD (2017) Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information. Inf Sci 399:64–80. https://doi.org/10.1016/j.ins.2017.02.051
Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367(1-2):52–61. https://doi.org/10.1016/j.jhydrol.2008.12.024
Drake JT (2000) Communications phase synchronization using the adaptive network fuzzy inference system (anfis). New Mexico State University Las Cruces, NM, USA
Ebtehaj I, Sattar AMA, Bonakdari H, Zaji AH (2016) Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. J Hydroinf 19(2):207–224. https://doi.org/10.2166/hydro.2016.025
Ebtehaj I, Bonakdari H, Moradi F, Gharabaghi B, Khozani ZS (2018) An integrated framework of extreme learning machines for predicting scour at pile groups in clear water condition. Coast Eng 135:1–15. https://doi.org/10.1016/j.coastaleng.2017.12.012
Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc., Upper Saddle River
Ghodsi H, Khanjani MJ, Beheshti A (2018) Evaluation of harmony search optimization to predict local scour depth around complex bridge piers. Civil Eng J 4(2):402–412. https://doi.org/10.28991/cej-0309100
Hamidi AR, Siadatmousavi SM (2018) (In press) Numerical simulation of scour and flow field for different arrangements of two piers using SSIIM model. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2017.03.012
Han D, Chan L, Zhu N (2007) Flood forecasting using support vector machines. J Hydroinf 9(4):267–276. https://doi.org/10.2166/hydro.2007.027
Hosseini R, Amini A (2015) Scour depth estimation methods around pile groups. KSCE J Civ Eng 19(7):2144–2156. https://doi.org/10.1007/s12205-015-0594-7
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
Kaya A (2010) Artificial neural network study of observed pattern of scour depth around bridge piers. Comput Geotech 37(3):413–418. https://doi.org/10.1016/j.compgeo.2009.10.003
Liao KW, Muto Y, Lin JY (2018) Scour depth evaluation of a bridge with a complex pier foundation. KSCE J Civ Eng 22(7):2241–2255. https://doi.org/10.1007/s12205-017-1769-1
Moussa AMA (2020) Evaluation of local scour around bridge piers for various geometrical shapes using mathematical models. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2017.08.003
Najafzadeh M (2015) Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions. Ocean Eng 99:85–94. https://doi.org/10.1016/j.oceaneng.2015.01.014
Najafzadeh M, Barani GA, Hessami-Kermani MR (2013) Group method of data handling to predict scour depth around vertical piles under regular waves. Sci Iran 20(3):406–413. https://doi.org/10.1016/j.scient.2013.04.005
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC-15(1):116–132. https://doi.org/10.1109/TSMC.1985.6313399
Vaghefi M, Mahmoodi K, Akbari M (2019a) Detection of outlier in 3D flow velocity collection in an open-channel bend using various data mining techniques. IJST-T Civ Eng 43(2):197–214. https://doi.org/10.1007/s40996-018-0131-2
Vaghefi M, Mahmoodi K, Setayeshi S, Akbari M (2019b) Application of artificial neural networks to predict flow velocity in a 180° sharp bend with and without a spur dike. Soft Comput 24:8805–8821. https://doi.org/10.1007/s00500-019-04413-5
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Wang H, Tang H, Liu O, Wang Y (2016) Local scouring around twin bridge piers in open channel flows. J Hydraul Eng ASCE 142(9):06016008. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001154
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhang Q, Zhou XL, Wang JH (2017) Numerical investigation of local scour around three adjacent piles with different arrangements under current. Ocean Eng 142:625–638. https://doi.org/10.1016/j.oceaneng.2017.07.045
Zounemat-Kermani M, Beheshti A, Ataie-Ashtiani B, Sabbagh-Yazdi SR (2009) Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Appl Soft Comput 9(2):746–755. https://doi.org/10.1016/j.asoc.2008.09.006
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Adib, A., Tabatabaee, S.H., Khademalrasoul, A. et al. Recognizing of the best different artificial intelligence method for determination of local scour depth around group piers in equilibrium time. Arab J Geosci 13, 1004 (2020). https://doi.org/10.1007/s12517-020-05738-4
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DOI: https://doi.org/10.1007/s12517-020-05738-4