Data-Driven Approach to Safety Control in Jacket-Launching Installation Operations

Abstract

Installing offshore wind jackets faces increasing risks from dynamic marine conditions and is challenged by trajectory deviations due to coupled hydrodynamic and environmental factors. To address the limitations of software, such as long simulation times and tedious parameter adjustments, this study develops a rapid prediction model combining Radial Basis Function (RBF) and Backpropagation (BP) neural networks. The model is enhanced by incorporating both numerical simulation data and real-world measurement data from the launching operation. The real-world data, including the barge attitude before launching, jacket weight distribution, and actual environmental conditions, are used to refine the model and guide the development of a fully parameterized adaptive controller. This controller adjusts in real time, with its performance validated against simulation results. A case study from the Pearl River Mouth Basin was conducted, where datasets—capturing termination time, six-degrees-of-freedom motion data for the barge and jacket, and actual environmental conditions—were collected and integrated into the RBF and BP models. Numerical models also revealed that wind and wave conditions significantly affected lateral displacement and rollover risks, with certain directions leading to heightened operational challenges. On the other hand, operations under more stable environmental conditions were found to be safer, although precautions were still necessary under strong environmental loads to prevent collisions between the jacket and the barge. This approach successfully reduces weather-dependent operational delays and structural load peaks. Hydrodynamic analysis highlights the importance of directional strategies in minimizing environmental impacts. The model’s efficiency, requiring a fraction of the time compared to traditional methods, makes it suitable for real-time applications. Overall, this method provides a scalable solution to enhance the resilience of marine operations in renewable energy projects, offering both computational efficiency and high predictive accuracy.