Cover page and Table of Contents: PDF (size: 1032KB)
Full Text (PDF, 1032KB), PP.38-47
Views: 0 Downloads: 0
Pier scour, clear water condition, live bed condition, Particle Swarm Optimization- Artificial Neural Network (PSO-ANN)
Scour around the bridge pier is one of the major factors which affect the safety and stability of the bridge structure. Due to the presence of complexity in the scour mechanism, there is no common and simple method to estimate the scour depth. The present paper gives an idea of hybridizing two techniques such as an artificial neural network with swarm intelligence technique particle swarm optimization to estimate the scour depth around the bridge pier and abbreviated as PSO-ANN. The present discussion covers the estimation of scour depth for clear water and live bed scour condition around circular and rectangular pier shapes. The independent variables, Sediment size (d50), sediment quantity (Sq), velocity (u) and time (t) are used as input to develop the models to estimate or quantify a dependent variable scour depth (ds). The efficiency and accuracy of the model are measured using model performances indicators such as Correlation Coefficient (CC), Normalized Root Mean Square Error (NRMSE), Nash Sutcliffe Error (NSE), and Normalized Mean Bias (NMB). The predicted results of both the models are compared with each other and also compared with measured scour depth. The study concludes that the proposed PSO-ANN model is suitable to estimate the scour depth in both the cases for circular and rectangular pier shapes.
Sreedhara B M, Geetha Kuntoji, Manu, S Mandal, "Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.11, pp.38-47, 2019. DOI:10.5815/ijisa.2019.11.04
L.J. Prendergast, K. Gavin, A review of bridge scour monitoring techniques, J. Rock Mech. Geotech. Eng. 6 (2014) 138–149. doi:10.1016/j.jrmge.2014.01.007.
B.M. Sreedhara, M. Rao, S. Mandal, Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers, Neural Comput. Appl. (2018). doi:10.1007/s00521-018-3570-6.
S. Akib, M. Mohammadhassani, A. Jahangirzadeh, Application of ANFIS and LR in prediction of scour depth in bridges, Comput. Fluids. (2014). doi:10.1016/j.compfluid.2013.12.004.
H.M. Azamathulla, A.A. Ghani, ANFIS-Based Approach for Predicting the Scour Depth at Culvert Outlets, J. Pipeline Syst. Eng. Pract. 2 (2011) 35–40. doi:10.1061/(ASCE)PS.1949-1204.0000066.
B.M. Sreedhara, Manu, S. Mandal, Estimation of live bed scour depth around different shapes of bridge piers using ANFIS and SVMR approach, Int. J. Ecol. Dev. 33 (2018) 30–46.
M. and M.S. Geetha S., Kuntoji, Subba Rao, Performance evaluation of ANFIS and SVM Model in Prediction of Wave Transmission over Submerged Reef of Tandem Breakwater, Int. J. Ecol. Dev. 32 (2017) 141–155.
M. Najafzadeh, G.A. Barani, Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers, Sci. Iran. (2011). doi:10.1016/j.scient.2011.11.017.
M. Najafzadeh, H.M. Azamathulla, Group method of data handling to predict scour depth around bridge piers, Neural Comput. Appl. (2013). doi:10.1007/s00521-012-1160-6.
A. Kaya, Artificial neural network study of observed pattern of scour depth around bridge piers, Comput. Geotech. (2010). doi:10.1016/j.compgeo.2009.10.003.
L.T. L, J.D. S, Z.G. H, Neural Network Modeling for estimation of Scour depth around Bridge piers, 19 (2007) 378–386.
Elhag, T. M., & Wang, Y. M. (2007). Risk assessment for bridge maintenance projects: neural networks versus regression techniques. Journal of computing in civil engineering, 21(6), 402-409..
H.M. Azamathulla, M.C. Deo, P.B. Deolalikar, Alternative neural networks to estimate the scour below spillways, Adv. Eng. Softw. (2008). doi:10.1016/j.advengsoft.2007.07.004.
M. Hasanipanah, M. Noorian bidgoli, • Danial, J. Armaghani, H. Khamesi, Feasibility of PSO‑ANN model for predicting surface settlement caused by tunneling, (n.d.) (2016) doi:10.1007/s00366-016-0447-0.
G. Hadi, K. Morteza, P. Enrique, F. Javier, F. Andrés, A new hybrid ANN model for evaluating the efficiency of the Π-type Floating Breakwater, Coast. Eng. Proc. 35 (2016) 25.
G. Kuntoji, M. Rao, S. Rao, Prediction of wave transmission over submerged reef of tandem breakwater using PSO-SVM and PSO-ANN techniques, ISH J. Hydraul. Eng. (2018) 1–8. doi:10.1080/09715010.2018.1482796.
N. Kayarvizhy, S. Kanmani, V. Rhymend Uthariaraj, Improving Fault Prediction using ANN-PSO in Object Oriented Systems, Int. J. Comput. Appl. 73 (2013) 18–25.
Goswami Pankaj, Evaluation of scour depth around bridge piers, Gauhati University, 2013. http://shodhganga.inflibnet.ac.in/handle/10603/74698.