Bridge-pier scour is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by the writers), in the form of artificial neural networks (ANNs) and genetic programming (GP). There had been 398 data sets of field measurements that were collected from published literature and were used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in the training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth at bridge piers.

This is a pioneer study that presents genetic programming (GP) as a new tool for prediction of local scour downstream of grade-control structures. The objective of this study is to provide an alternative formulation to conventional regression based equations and verify the superiority of GP over regression analysis. The training and testing patterns of the proposed GP formulation are based on well established and widely dispersed experimental results from the literature. Linear and nonlinear regression-based equations were derived throughout regression analysis on dimensionless parameters obtained from dimensional analysis. The GP-based formulation results are compared with experimental results and other equations and found to be more accurate.

This study presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to modeling stage–discharge relationship. The results obtained are compared to more conventional methods, stage rating curve and multiple linear regression techniques. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient, the coefficient of efficiency, and the adjusted coefficient of efficiency are used to measure the performance of the models developed by employing GEP. Also, the explicit formulations of the developed GEP models are presented. Statistics and scatter plots indicate that the proposed equations produce quite satisfactory results and perform superior to conventional models.