Hybrid computational model for predicting bridge scour depth near piers and abutments

• Estimation of scour depth is essential for designing reliable bridges.
• This paper presents a GA-SVR model to enhance the accuracy of predicting scour depth.
• A method combining a genetic algorithm and support vector regression is proposed.
• The method can be employed to design safe and cost-effective bridge substructures.

Efficient bridge design and maintenance requires a clear understanding of channel bottom scouring near piers and abutment foundations. Bridge scour, a dynamic phenomenon that varies according to numerous factors (e.g., water depth, flow angle and strength, pier and abutment shape and width, material properties of the sediment), is a major cause of bridge failure and is critical to the total construction and maintenance costs of bridge building. Accurately estimating the equilibrium depths of local scouring near piers and abutments is vital for bridge design and management. Therefore, an efficient technique that can be used to enhance the estimation capability, safety, and cost reduction when designing and managing bridge projects is required. This study investigated the potential use of genetic algorithm (GA)-based support vector regression (SVR) model to predict bridge scour depth near piers and abutments. An SVR model developed by using MATLAB® was optimized using a GA, maximizing generalization performance. Data collected from the literature were used to evaluate the bridge scour depth prediction accuracy of the hybrid model. To demonstrate the capability of the computational model, the GA–SVR modeling results were compared with those obtained using numeric predictive models (i.e., classification and regression tree, chi-squared automatic interaction detector, multiple regression, artificial neural network, and ensemble models) and empirical methods. The proposed hybrid model achieved error rates that were 81.3% to 96.4% more accurate than those obtained using other methods. The GA–SVR model effectively outperformed existing methods and can be used by civil engineers to efficiently design safer and more cost-effective bridge substructures.