Abstract
The mechanism of scour around the bridge pier is a complex phenomenon, and it is very difficult to make a common method to predict or estimate the depth of scour hole. In this paper, a hybrid model is developed, combining support vector machine and particle swarm optimization (PSO-SVM) to predict scour depth around a bridge pier. The input parameters such as sediment size (d50), the velocity of flow (U), and time (t) are used in the study to predict the scour depth. The models are developed with RBF, polynomial, and linear kernel functions, and the performances are evaluated using different statistical parameters such as CC, RMSE, NSE, and NMB. The predicted results are compared with measured scour depth. The predicted scour depth reveals that PSO-SVM with RBF kernel function model is found to be reliable and efficient in predicting the scour depth around bridge piers.
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References
Abdalla, M.G.: A study on scour for irrigation canals in egypt, “case study: the first reach of El-ibrahimeya canal.” Am. J. Eng. Technol. Manag. 1, 65–77 (2016). https://doi.org/10.11648/j.ajetm.20160104.13
Cus, F., Balic, J., Zuperl, U.: Hybrid ANFIS-ants system based optimisation of turning parameters. J. Achiev. Mater. Manuf. Eng. 36(1), 79–86 (2009)
Akib, S., Mohammadhassani, M., Jahangirzadeh, A.: Application of ANFIS and LR in prediction of scour depth in bridges. Comput. Fluids (2014). https://doi.org/10.1016/j.compfluid.2013.12.004
Chou, J.S., Pham, A.D.: Hybrid computational model for predicting bridge scour depth near piers and abutments. Autom. Constr. (2014). https://doi.org/10.1016/j.autcon.2014.08.006
Najafzadeh, M., Barani, G.A.: Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Sci. Iran. (2011). https://doi.org/10.1016/j.scient.2011.11.017
Hasanipanah, M., Noorian-Bidgoli, M., Armaghani, J.D., Khamesi, H.: Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling (2016). https://doi.org/10.1007/s00366-016-0447-0
Annaty, M., Eghbalzadeh, A., Hosseini, S.: Hybrid ANFIS model for predicting scour depth using particle swarm optimization. Indian J. Sci. Technol. 8, 326–332 (2015). https://doi.org/10.17485/ijst/2015/v8i
Basser, H., Karami, H., Shamshirband, S., Akib, S., Amirmojahedi, M., Ahmad, R., et al.: Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike. Appl. Soft Comput. J. (2015). https://doi.org/10.1016/j.asoc.2015.02.011
Pankaj, G.: Evaluation of scour depth around bridge piers. Gauhati University (2013)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1023/A:1022627411411
Zhang, X., Guo, Y.: Optimization of SVM parameters based on PSO algorithm. In: 2009 Fifth International Conference on Natural Computation, vol. 1, pp. 536–539 (2009). https://doi.org/10.1109/icnc.2009.257
Mahjoobi, J., Mosabbeb, E.A.: Prediction of significant wave height using regressive support vector machines. Ocean Eng. 36, 339–347 (2009). https://doi.org/10.1016/j.oceaneng.2009.01.001
Mandal, S., Rao, S., Harish, N., Lokesha: Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models. Int. J. Nav. Archit. Ocean Eng. 4, 112–122 (2012). https://doi.org/10.3744/jnaoe.2012.4.2.112
Ivanciuc, O.: Applications of support vector machines in chemistry. Rev. Comput. Chem. 23, 291–400 (2007). https://doi.org/10.1002/9780470116449.ch6
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 1995 IEEE International Conference on Neural Networks (ICNN’95), 1942–1948, vol. 4 (1995). https://doi.org/10.1109/icnn.1995.488968
Harish, N., Mandal, S., Rao, S., Patil, S.G.: Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater. Appl. Soft Comput. J. 27, 313–321 (2015). https://doi.org/10.1016/j.asoc.2014.10.041
Acknowledgements
The authors would like to express their sincere gratitude to Dr. Goswami Pankaj, Gauhati University, for providing experimental data. Also, grateful to Director and Head of the department, Applied Mechanics and Hydraulics department, NITK, Surathkal for necessary support.
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Sreedhara, B.M., Manu, Mandal, S. (2019). Swarm Intelligence-Based Support Vector Machine (PSO-SVM) Approach in the Prediction of Scour Depth Around the Bridge Pier. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_36
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DOI: https://doi.org/10.1007/978-981-13-1595-4_36
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