ANFIS approach to the scour depth prediction at a bridge abutment

Abstract

An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour counter-measure to be implemented against excessive scouring. Despite analysis of innumerable prototype and hydraulic model studies in the past, the scour depth prediction at the bridge abutments has remained inconclusive. This paper presents an alternative to the conventional regression model (RM) in the form of an adaptive network-based fuzzy inference system (ANFlS) modelling. The performance of ANFIS over RM and artificial neural networks (ANNs) is assessed here. It was found that the ANFlS model performed best among of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form. © IWA Publishing 2010.