Vibration-based structural health monitoring methods have been widely applied in the field of damage identification. This paper proposes an intelligent diagnostic approach that integrates a multi-scale convolutional neural network with a multi-head attention mechanism (MSCNN-MHA) for the structural health monitoring of jacket-type offshore platforms. Through numerical simulations, acceleration response signals of three-pile and four-pile jacket platforms under random wave excitation are analyzed. Damage localization studies are conducted under simulated crack and pitting corrosion cases. Unlike previous studies that often idealize damage by weakening structural parameters or removing components, this study focuses on small-scale damage forms to better reflect real engineering conditions. To verify the noise resistance of the proposed method, noise is added to the original signals for further testing. Finally, experiments are conducted on the basic structure of the jacket-type offshore platform, simulating small-scale crack and pitting damage under sinusoidal and pulse excitation, to further evaluate the applicability of the method. Compared to previous CNN and MSCNN-based approaches, the results of this study demonstrate that the MSCNN-MHA method achieves higher accuracy in identifying and locating minor damage in jacket-type offshore platforms.

With the continuous exploitation of marine fishery resources, the side-by-side mooring of fishing vessels in harbors has become increasingly common. However, under complex wind and wave conditions, the safety of the mooring is directly affected by the motion response of fishing vessels and the loading characteristics of the mooring system. A reasonable mooring configuration is crucial for reducing motion amplitudes, balancing load distribution, and enhancing overall stability. This study established a model of five fishing vessels using ANSYS-AQWA. In this configuration, the outermost and central vessels are large-scale fishing vessels, and small-scale fishing vessels are placed between each pair of large-scale vessels. By comparing different mooring strategies, the study systematically investigates the motion responses of the fishing vessels and the load characteristics of the mooring system, as well as the impact of wind–wave angles. The results indicate that the mooring configurations significantly influence the surge, sway, roll, and yaw responses of the fishing vessels. The configuration with three mooring lines appears to be the effective in reducing the amplitudes of translational motions and demonstrates higher reliability in mitigating the forces on the mooring system. Furthermore, as the wind–wave angle increases, the sway and yaw responses of the fishing vessels exhibit a significant rise. Meanwhile, the mooring lines, fenders, and hawsers at different positions demonstrate distinct load characteristics. These findings provide valuable insights for the safety assessment of fishing vessel mooring and the optimization of mooring system design.

Oscillating hydrofoils play a crucial role in tidal energy conversion systems, with their structural design exerting a profound influence on the hydrodynamic efficiency and overall performance of these devices. Inspired by the morphology of fish caudal fins, this study designs four biomimetic hydrofoils with diverse structural configurations to investigate their hydrodynamic performance: leading edge swept-back, trailing edge swept-forward, swept-back, and swept-forward. Computational fluid dynamics simulations are utilized to systematically analyze the impacts of sweep angles, pitch axis locations, heave amplitudes, and pitch angles on the hydrodynamic performance of these hydrofoil configurations. The results indicate that increasing the sweep angle significantly improves the lift-to-drag ratio of the hydrofoil, with the drag coefficient of the trailing-edge swept-forward configuration reduced by 12% as the sweep angle increases from 5° to 35°. Power output can be enhanced by adjusting the pitch axis location toward the mid-chord. Increasing the heave amplitude improves the lift-to-drag ratio but reduces the average power. At a pitch amplitude of 75°, the growth rate of the lift coefficient slows down, while that of the drag coefficient remains unchanged. Meanwhile, the average power coefficient reaches its maximum. This study contributes to the understanding of efficient hydrofoil design and highlights the importance of incorporating coupled motion parameters in computational models to better capture hydrofoil dynamics in future research.

Directly measuring the mooring cable load while a ship is moored at a wharf poses significant practical challenges. This paper proposes an indirect load measurement method to identify mooring cable static loads based on the Influence Coefficient Matrix (ICM) method. First, a finite element analysis of the bollard is conducted to obtain the full-field strains under each unit load. A solution procedure based on the genetic algorithm (GA) is then implemented to determine the optimal placement and orientation of strain gauges, aiming to improve load identification accuracy. An optimal load coefficient matrix is derived to establish the correlation between cable loads and bollard strains. Subsequently, following the established measured point placement scheme, strain gauges are installed on the bollard surface to capture the strains, enabling inverse identification of mooring cable loads through the measured strains and the pre-established load–strain relationship. A numerical case study validated the feasibility of this method, demonstrating high identification accuracy. Furthermore, experimental verification was conducted to assess its reliability under different conditions. Results confirmed the effectiveness of this indirect approach for mooring cable static loads measurement. The research findings provide a technical framework for real-time monitoring of mooring cable loads.