Discussion: Bridge pier scour prediction by gene expression programming

Abstract

is highly sensitive to the Froude number, F r , and tends towards infinity as F r approaches 1 . 0, so that the equation is meaningless for near-critical or supercritical flows. In natural rivers, F r generally correlates fairly well with grain size and does not exert an important independent influence; in fact, many experimentally based pier scour relationships discount it completely.The inadequacy of Equation 12 as a predictor is also demonstrated by the authors' Figure 7, in which the predicted values of their ratio d s /Y can range from about 0 . 25 to 2 . 5 times the observed value. It thus appears that Equation 12is not a useful predictor of design scour depths for river bridge piers and could lead to gross under-prediction or over-prediction in practical cases. Much more reliable predictive relationships, generally based on experiments but not significantly challenged by field experience, have been available for many years. The assertion made by the authors in their conclusions, regarding the utility of their model to engineers and planners, is not supported by the examples provided above. Authors' replyIn response to the first of the contributors' comments, namely that using d s /Y as primary variable will not result in better models, the authors developed a GEP-based model using d s /b as primary variable (resulting equation presented as Equation 13 below) and found that the values of the coefficient of determination, R 2 , were very low (0 . 35 for training and 0 . 25 for the validation data set) when compared with the GEP-based models developed for the same data sets using d s /Y as primary variables, for which the R 2 values were 0 . 76 and 0 . 74 for training and validation data sets, respectively. The model that was developed

Editorial

“…The issue closes with two interesting discussions on estimation of the Muskingum parameter for flood routing models (Orouji et al, 2014) and bridge pier scour prediction by gene expression programming (Khan et al, 2014a). Orouji et al (2013) used two meta-heuristic algorithms to calibrate the parameters of a nonlinear Muskingum model.…”

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

“…Raikar et al (2016) applied ANN and genetic algorithms (GA) for prediction of scour depth within channel contractions. Many researchers also verified the efficacy of techniques like adaptive neuro-fuzzy inference system (ANFIS) and support vector machines (SVM) for predicting bridge scour (Bateni et al 2007;Muzzammil 2010;Ghazanfari-Hashemi et al 2011;Pal et al 2011;Hong et al 2012;Akib et al 2014;Khan et al 2014;Najafzadeh et al 2016;Chou & Pham 2017). More recently, several studies have reported the use of hybrid techniques for predicting bridge scour (Chou & Pham 2014;Jannaty et al 2015;Dang et al 2019).…”