Estimation of Local Scour Around Monopile Foundations for Offshore Structures Using Machine Learning Models

Scour around monopile foundations is one of the most important phenomena that causes instability of marine structures. Machine learning methods are currently widely used to describe the complex physics of flowsediment-structure interactions, but their application to local scour around piers and monopile foundations remains a challenge. In this study, three machine learning algorithms, including backpropagation neural networks (BPNN), radial basis function neural network (RBFNN), and support vector machines (SVM), are used to solve the problem of predicting local scour depth around pile foundations for offshore structures in the marine environment. Both the dimensional and dimensionless data combinations are considered to build the models. After the optimal configuration is determined, the models are used to predict the scour depth under pure-wave and combined wave and current conditions, and the statistical indices show the higher accuracy of the SVM model under either data combination. Moreover, the thus obtained normalized results are compared with those computed using validated empirical equations. Results of the sensitivity analysis indicate that the ratio of velocities (Ucw) is the most effective parameter for predicting the scour depth around monopile foundations in marine environment, and the findings on parameter investigations were in good agreement with the results from laboratory experiments.