عنوان مقاله [English]
Rivers accumulate huge amounts of floating debris including the trunk, branches and leaves during the floods, leading to increase the depth of local scour around bridge piers. A large number of the laboratorial and field studies have been performed to understand the mechanism of scouring phenomenon under floating debris. Over two past decades, different types of the artificial intelligence methods have been used to estimate the maximum scour depth around bridges piers. In this study, the Neuro-Fuzzy model based on group method data handling (NF-GMDH) was used to estimate the scour under effect of debris accumulations. The NF-GMDH network was developed using evolutional algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and gravitational search algorithm (GSA). Parameters effective on the maximum scour depth included average velocity of upstream flow of the bridge pier, critical velocity of river bed sediments, depth of flow in section without debris, thickness of submerged debris, debris diameter, average particle size, pier diameter, and channel width. After training and experiencing each NF-GMDH models, the performances of each one was evaluated through statistical parameters. The results showed that the models proposed had better performance compared with emperical relationships. NF-GMDH-PSO (R=0.8413 and RMSE=0.37) and NF-GMDH-GA (R=0.8407 and RMSE=0.3640) had relatively similar performance. Finally, sensitivity analysis indicated that the ratio of pile diameter (D) to mean diameter of bed sediments (d50) has the most influence on determination of maximum scour depth.
Azamathulla H. Md., Ghani A. A., Zakaria N.A. and Guven A. (2010). Genetic programming to predict bridge pier scour. J. Hydraul. Eng., 136 (3), 165-169.
Bateni S. M., and Jeng D. S. (2007). Estimation of pile group using adaptive neuro-fuzzy approach. Ocean. Eng., 34(8-9), 1344-1354.
Etemad-Shahidi A. and Ghaemi, N. (2011). Model tree approach for prediction of pile groups scour due to waves. Ocean. Eng., 38(13), 1522-1527.
Holland, J. H. (1976). Adaptation, “Progress in Theoretical Biology, 4,” R. Rosen and F. M. Snell, eds., Academic Press, New York.
Keshavarz-Mehr M. (2012). Neural Networks, Fuzzy Logic, and Genetic Algorithm: Combination and application. Noorpardazan Press [In Persian].
Kennedy J. and Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks 4.
Lagasse P. F., Zevenbergan L. W. and Clopper P. E. (2010). Impacts of Debris on Bridge Pier Scour. Proceedings of the 33rd IAHR Congress, IAHR, Madrid, 3967-3974.
Laursen E. M. and Toch A. (1956). Scour around bridge piers and abutments. Iowa Highway Research Board Ames, IA, USA.
Melville B. W. and Sutherland A. J. (1988). Design method for local scour at bridge piers. J. Hydraul. Eng., 114 (10), 1210-1226.
Melville B. W. and Dongol D. M. (1992). Bridge pier scour with debris accumulation. J. Hydraul. Eng., 118 (9), 1306-1310.
Melville B. W. and Chiew Y. M. (1999). Time scale for local scour at bridge piers. J. Hydraul. Eng., 125(1), 59-65.
Nagasaka K., Ichihashi H. and Leonard R. (1995). Neuro-fuzzy GMDH and its application to modeling grinding characteristics. Int. J. Prod. Res., Production Research. 33(5), 1229-1240.
Najafzadeh M., Barani G. A. and Hessami M. R. (2013a). Group method of data handling to predict scour depth around vertical piles under regular waves. Iran. J. Sci. Technol., 20(3), 406-413.
Najafzadeh M., Barani G. A. and Hessami M. R. (2013b). Abutment scour in clear-water and live-bed conditions by GMDH network. Water. Sci. Technol., 67(5), 1121-1128.
Najafzadeh M., Barani G. A. and Hessami M. R. (2014). Group method of data handling to predict scour at downstream of a ski-jump bucket spillway. J. Earth.Inform., 7(4), 231-248.
Najafzadeh M., Barani G. A. and Hessami M. R. (2015). Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds. Ocean. Eng., 104, 387-396.
Najafzadeh M. (2015). Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions. Ocean. Eng., 99, 85-4.
Najafzadeh M. and Lim S. Y. (2015). Application of improved neuro-fuzzy GMDH to predict scour
depth at sluice gates. Earth. Sci. Inform., 8(1), 187-196.
Pagliara S. and Carnacina I. (2011). Influence of Wood Debris Accumulation on Bridge Pier Scour. J. Hydraul. Eng., 137(2), 254-261.
Rashedi E., Nezamabadi-pour H. and Saryazdi S. (2009). GSA: a gravitational search algorithm. J. Inform. Sci., 179 (13), 2232-2248.
Takashi O., Hidetomo I., Tetsuya M. and Kazunori N. (1998). Orthogonal and successive projection methods for the learning of neurofuzzy GMDH. J. Inform. Sci., 10 (1-2), 5-24.
Zounemat-Kermani M., Beheshti A. A., Ataie-Ashtiani B. and Sabbagh-Yazdia S. R. (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.