Visual deep learning with physics constraints for local scour evolution prediction at monopiles

Abstract

Local scour threatens the safety of marine structures, necessitating the precise prediction of scour evolution around these structures. A visually oriented deep learning model, called Disentangled Physics-constrained Prediction (DPP), was proposed in this study to predict scour evolution at monopiles reliably. It integrates scouring physics with advanced video prediction techniques through a two-branch architecture. The Physics-constrained Recurrent Module (PRModule) branch leverages Recurrent Neural Networks (RNNs) for temporal differentiation, ensuring accurate prediction of scouring-related physical information. Meanwhile, the Convolutional Long-Short-Term Memory (ConvLSTM) branch captures spatial and temporal dynamics in scouring videos, focusing on the prediction of residual features. DPP outperformed three baseline models in predicting the scour evolution at monopiles. Across three scouring scenarios, DPP achieved a 14.2% decrease in Root Mean Squared Error, a 14.7% reduction in Mean Absolute Error, and an 8.1% increase in Structural Similarity on average, compared to the best-performing baseline model. The predicted scouring frames are found to agree well with the true frames, demonstrating DPP's potential as a valuable tool to protect marine infrastructures.