Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

  • Yousef Hassanzadeh University of Tabriz
  • Amin Jafari-Bavil-Olyaei University of Tabriz
  • Mohammad Taghi-Aalami
  • Nazila Kardan

Abstract

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive network-based fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods.

Keywords: adaptive neuro-fuzzy inference system, colliding bodies optimization, empirical equations, scour depth, vibrating particles system
Published online
2019-08-14
How to Cite
Hassanzadeh, Y., Jafari-Bavil-Olyaei, A., Taghi-Aalami, M., & Kardan, N. (2019). Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers. Periodica Polytechnica Civil Engineering, 63(3), 856-871. https://doi.org/10.3311/PPci.12777
Section
Research Article