Study on Motion Response Prediction of Offshore Platform Based on Multi-Sea State Samples and EMD Algorithm

The complexity of offshore operations demands that offshore platforms withstand the variability and uncertainty of marine environments. Consequently, analyses of platform motion responses must extend beyond single sea state conditions. This study employs the Computational Fluid Dynamics (CFDs) software STAR-CCM+ for data acquisition and investigates platform motion from two perspectives: adaptability analysis to different wave directions and adaptability analysis to varying significant wave heights. The aim is to develop a model capable of predicting offshore platform motion responses across multiple sea state conditions. The results demonstrate that integrating the empirical mode decomposition (EMD) algorithm with residual convolutional neural networks (ResCNNs) and Long Short-Term Memory (LSTM) networks effectively resolves the challenge of insufficient prediction accuracy under diverse maritime conditions. Following EMD incorporation, the model’s performance within the predictive range was significantly enhanced, with the coefficient of determination (R2) consistently exceeding 0.5, indicating a high degree of model fit to the data. Concurrently, the mean squared error (MSE) and Mean Absolute Percentage Error (MAPE) metrics exhibited commendable performance, further substantiating the model’s precision and reliability. This methodology introduces an innovative approach for forecasting the dynamic responses of offshore structures, providing a more rigorous and accurate foundation for maritime operational decisions. Ultimately, the research enhances the safety and productivity of offshore activities.