Abstract
Scour is one of the major factors which affects directly on the durability and safety of the Bridge abutments. Based on the experimental data of Goswami in 2012, an effort is made to predict local scour by using a hybrid approach of Swarm Intelligence based algorithms which is today one of the powerful tools of optimization techniques. In this work, an intelligent model based on support vector machine in combination with the particle swarm optimization (PSO-SVM) technique is developed. The PSO-SVM models are developed with RBF, Polynomial and Linear kernel functions. The circular, rectangular, round-nosed, and sharp-nosed shapes of piers are considered in live bed scour condition. The scour depth around bridge piers is predicted by considering Sediment size, flow velocity, and time of flow as input parameters. Prediction accuracy of the models is evaluated using the model performance indicators such as Root Mean Square Error (RMSE, Correlation Coefficient (CC), Nash Succlift Error (NSE), etc. The results obtained from the model are compared with the measured scour depth to validate the reliability of the hybrid model. Based on the results, PSO based SVM model is found to be successful, reliable, and efficient in predicting the scour depth around the bridge pier.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sheppard, D.M., Miller Jr., W.: Live-bed local pier scour experiments. J. Hydraul. Eng. 132(7), 635–642 (2006)
Ballio, F., Radice, A., Dey, S.: Temporal scales for live-bed scour at abutments. J. Hydraul. Eng. 136(7), 395–402 (2009)
Ettmer, B., Orth, F., Link, O.: Live-bed scour at bridge piers in a lightweight polystyrene bed. J. Hydraul. Eng. 141(9), 04015017 (2015)
Najafzadeh, M., Barani, G.A., Azamathulla, H.M.: Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Comput. Appl. 24(3–4), 629–635 (2014)
Balouchi, B., Nikoo, M.R., Adamowski, J.: Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: application of different types of ANNs and the M5P model tree. Appl. Soft Comput. 34, 51–59 (2015)
Hong, J.H., Goyal, M.K., Chiew, Y.M., Chua, L.H.: Predicting time-dependent pier scour depth with support vector regression. J. Hydrol. 468, 241–248 (2012)
Cus, F., Balic, J., Zuperl, U.: Hybrid ANFIS-ants system based optimisation of turning parameters. J. Achievements Mater. Manufact. 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 91, 77–86 (2014)
Chou, J.S., Pham, A.D.: Hybrid computational model for predicting bridge scour depth near piers and abutments. Autom. Constr. 48, 88–96 (2014)
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. Iranica 18(6), 1207–1213 (2011)
Hasanipanah, M., Noorian-Bidgoli, M., Armaghani, D.J., Khamesi, H.: Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng. Comput. 32(4), 705–715 (2016)
Jannaty, M.H., Eghbalzadeh, A., Hosseini, S.A.: Hybrid ANFIS model for predicting scour depth using particle swarm optimization. Indian J. Sci. Technol. 8(22) (2015)
Basser, H., Karami, H., Shamshirband, S., Akib, S., Amirmojahedi, M., Ahmad, R., Javidnia, H.: Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike. Appl. Soft Comput. 30, 642–649 (2015)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media (2013)
Mandal, S., Rao, S., Harish, N.: Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models. Int. J. Naval Archit. Ocean Eng. 4(2), 112–122 (2012)
Mahjoobi, J., Mosabbeb, E.A.: Prediction of significant wave height using regressive support vector machines. Ocean Eng. 36(5), 339–347 (2009)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks IV, vol. 1000 (1995, November)
Acknowledgements
The authors would like to express their sincere gratitude to Dr. Goswami Pankaj, Guwahati University for providing experimental data. Also, grateful to Director and Head of the department, Applied Mechanics and Hydraulics, NITK, Surathkal for necessary support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sreedhara, B.M., Kuntoji, G., Manu, Mandal, S. (2019). PSO-SVM Approach in the Prediction of Scour Depth Around Different Shapes of Bridge Pier in Live Bed Scour Condition. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_37
Download citation
DOI: https://doi.org/10.1007/978-981-13-0761-4_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0760-7
Online ISBN: 978-981-13-0761-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)