Study on the Prediction of Motion Response of Offshore Platforms Based on ResCNN-LSTM

• Published in: Journal of marine science and engineering Vol. 12; no. 10; p. 1869
• Main Authors: Diao, Feng, Liu, Tianyu, Likeufack Mdemaya, Franck Aurel, Xu, Gang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Big Data; Chaos theory; Decision making; Deep learning; Drilling platforms; Error analysis; Forecasts and trends; Irregular waves; Long short-term memory; Machine learning; motion response; Movement; Naval architecture; Naval engineering; Neural networks; Ocean engineering; Offshore engineering; offshore platform; Offshore platforms; Offshore structures; Prediction models; Predictions; Regular waves; Sea currents; Sea level; Shipbuilding; Time series
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12101869

In the random sea environment, offshore platforms are influenced by factors such as wind, waves, and currents, as well as their interactions, leading to complex motion phenomena that affect the safety of offshore platform operations. Consequently, accurately predicting the motion response of offshore platforms has long been a key focus in the fields of naval architecture and ocean engineering. This paper utilizes STAR-CCM+ to simulate time-history data of offshore platform motion responses under both regular and irregular waves. Furthermore, a predictive model combining residual convolutional neural networks and long short-term memory neural networks using neural network technology is also studied. This model utilizes an autoregressive approach to predict the motion responses of offshore platforms, with its predictive accuracy validated through comprehensive evaluations. Under regular wave conditions, the coefficient of determination (R2) for the platform’s heave and pitch responses consistently exceeds 0.99. Meanwhile, under irregular wave conditions, the R2 values remain generally above 0.4. Additionally, the model exhibits commendable performance in terms of Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. The aim of this study is to present a novel approach to predicting offshore platform motion responses, while providing a more scientific basis for decision-making in offshore platform operations.