I.J. Intelligent Systems and Applications, 2019, 11, 38-47 Published Online November 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2019.11.04 Copyright © 2019 MECS I.J. Intelligent Systems and Applications, 2019, 11, 38-47 Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier Sreedhara B M Assistant Professor, B M S Institute of Technology and Management, Bengaluru - 560064, India E-mail: bmshreedhar@gmail.com Geetha Kuntoji Assistant Professor, B M S College of Engineering, Bengaluru - 560019, India E-mail: geeta.kuntoji@gmail.com Manu Assistant Professor, National Institute of Technology Karnataka Surathkal, Mangaluru – 575025, India E-mail: manunitk77@gmail.com S Mandal Professor, Presidency University, Bengaluru – 560085, India E-mail: smandal12341@rediffmail.com Received: 29 July 2018; Accepted: 12 August 2019; Published: 08 November 2019 Abstract—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 (d 50 ), 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. Index Terms—Pier scour, clear water condition, live bed condition, Particle Swarm Optimization- Artificial Neural Network (PSO-ANN). I. INTRODUCTION Scour is the complex phenomenon occurs at the structure base located across the flowing water. Later, lowering of the riverbed leads to the exposure of bridge foundation. Scour mechanism occurs when the normal flow interacts with the obstacle and develops large scale eddies at the structure base. This leads to the formation of horseshoes and wake vortex at the base and downstream of the structure respectively. This vortex system leads to the erosion and forms scour hole. The typical scour mechanism is illustrated in Fig.1. Fig.1. Mechanism of local scour [1]