A novel ensemble model using artificial neural network for predicting wave-induced forces on coastal bridge decks

Due to the effects of climate change, coastal engineering structures are more vulnerable to the wave forces caused by natural hazards, especially for low-lying bridges. To facilitate the structural design and risk assessment of coastal bridges under extreme events, it is imperative to efficiently predict the wave-induced forces with high accuracy. In this study, a novel predictive model for wave-induced forces is established using the ensemble learning technique. Specifically, four state-of-the-art surrogate models, namely the support vector regression (SVR), Kriging (KRG), polynomial chaos expansion (PCE), and decision tree (DT) are employed to construct a weighted predictive model, where the weights of individual models are implicitly determined by the artificial neural network (ANN). Depending on the architecture of the ANN model, e.g., with or without a hidden layer, these four surrogate models can be ensembled nonlinearly (ANN1) or linearly (ANN2). Four benchmark functions and two ocean engineering cases are utilized to validate the performance of the established ensemble models. The coefficient of determination R², maximum absolute error (MAE), and root mean square error (RMSE) are used as the error metrics. The results show that the proposed ANN-based ensemble strategy is capable of providing robust and accurate approximation for different force components; it can effectively reduce the adverse effect of poorly behaved candidate surrogates by wisely assigning weights to the individual models, which is beneficial to protect against the use of the worst surrogate model. It is envisioned that the proposed ensemble models can be extended to predict wave forces of unstable wave conditions, thus facilitating the associated risk assessment and structural design of ocean infrastructure assets.