Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods

Scour is a major issue which impacts the life of a hydraulic structure. In this work, we have considered a bridge as an example of a hydraulic structure. Scour depth increases or decreases from time to time because of variations in flow scenarios. Scour phenomena have been extensively studied and many empirical as well as data-driven approaches have been proposed to estimate the scour depth. However, there are relatively few studies which can predict the time-dependent scour depth. In this study, we use a laboratory dataset compiled from several sources to predict time-dependent scour depth using ensemble and standalone machine learning methods. For scour depth estimation, various factors such as river bed properties ( d 5 0 ), s i g m a g , r h o s , U c r ), flow properties around a pier ( r h o f , U , m u , g , y ), bridge pier geometry ( A l , S h , D p ), and time ( t , t R ) are used. In this study, we apply two ensemble methods: Bagging Regressor (BR), AdaBoost Regressor (ABR), and one standalone machine learning method, Support Vector Regression (SVR), for the prediction of time-dependent scour depth. Out of these machine learning methods, both the ensemble methods provide superior predictions in comparison to the standalone variant and empirical equations, viz., Bagging Regressor ( r 2 = 0.913), AdaBoost Regressor ( r 2 = 0.887), followed by Support Vector Regressor ( r 2 = 0.814). We have also developed an open-source web-based tool to predict the time-dependent scour depth with the proposed machine learning methods. The web-based tool is generic enough to work with any dataset and allows the end user to select various input combinations, visualizations, and error metrics.