A series of CFD RANS simulations are presented for Wigley hulls of two freeboard heights progressing with forward speed in waves. Free surface effects are captured using the Volume of Fluid (VOF) method embedded in open source software OpenFOAM. Comparisons of heave, pitch motions and added resistance of the first Wigley model against the experiments of Kashiwagi (2013) confirm the numerical validity of the hydrodynamic modelling approach. Further simulations for the lower-freeboard Wigley model reveal that the highest green water impact on decks appears in way of $\lambda /L=1.3$ and at the highest instantaneous pitch amplitude where the water propagates far downstream and across the deck. The simulations also demonstrate that the green water events are associated with air bubble entrapment.

This study utilizes the inverse finite element method (iFEM) to investigate the strain field reconstruction of ship stiffened plates under multiple loading conditions. The aim is to enhance the monitoring, safety, and reliability of ship structures through multi-condition strain field reconstruction. By applying iFEM, this research addresses the challenge of reconstructing strain fields from discrete strain measurements using a least-squares variational equation derived from elastic mechanics principles. The performance of iFEM was evaluated under five loading conditions: axial compression, non-uniform loading, torsion, combined axial compression with non-uniform loading, and combined axial compression with symmetric uniform loading. To mitigate boundary effects, an extended stiffened plate design was implemented. The results show significant improvements in reconstruction accuracy: under two specific loading conditions, the precision improved by 38.82% and 11.25%, respectively, compared to the original plate. This study underscores the potential of iFEM in improving the monitoring and safety of marine structures. Future work could explore the applicability of iFEM to other marine structures and scenarios, ensuring broader practical applications.

This paper proposes an approach that utilizes Artificial-Intelligence-Generated Content (AIGC) to overcome the constraints of Structural Health Monitoring (SHM) devices in capturing global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An Artificial Neural Network (ANN) is used to establish the relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize its performance by considering factors such as the number of sensors, the neural network framework, and the convergence criteria. The generalization performance of the ANN is validated through a real-scale model test, with deviations below 10% and an average deviation of less than 4% in multiple conditions, verifying its accuracy. This technology represents a significant advancement, enhancing the practicality of the SHM technology from “point monitoring” to “field monitoring”.

The box girder’s condition significantly impacts the safety and overall performance of the entire ship because it is the primary stress component of the hull construction. This work used experimental research on the long-span hull box girder based on IFEM (Inverse Finite Element Method) technology to ensure the structural safety of the hull box girder. Due to the limitations of conventional experiments in this technical field, such as their reliance on finite element data and lack of input from physical tests, numerous research methods combining the strain sensing data from physical tests with the strain data from virtual sensors were conducted. The strain fields of the top plate, side plate, and bottom plate were each reconstructed in turn, and the verifier measuring points in the physical model test were used to assess the accuracy of the reconstruction results. The findings demonstrate that the top plate, side plate, and bottom plate reconstructions had relative errors of 0.24–7.86%, 0.75–8.13%, and 3.31–2.52%, respectively. This enables the reconstruction of the strain field of the long-span hull box girder using physical test data and promotes the use of iFEM technology in the field of structural health monitoring of large marine structures.

The saturated impulse is a special phenomenon in the dynamic plastic behavior of engineering structures under intensive pulse loading, such as slamming loading. In this study, slamming experiments were performed on steel plates to investigate their slamming pressure and dynamic plastic responses, as well as the saturation phenomenon, and elucidate the effect of the plate thickness and material properties on the dimensionless saturated deflection and saturated impulse in combination with the published test data. The results show that the dimensionless saturated deflection and saturated impulse of the test plates gradually increased as the dimensionless stiffness decreased. After being validated against the experimental results, a numerical method that considered the fluid–structure interaction (FSI) effect was then employed to provide comprehensive insight into the transient plastic responses and saturated impulse of the flat plates under slamming impact. Numerical simulations revealed that the compressed air layer always existed during the effective process of the flat slamming impact. Through the numerical prediction of the dynamic plastic deflection and slamming pulse loading, it was observed that the saturated impulse phenomenon always took place after the time instant of the peak value of the pressure pulse. Furthermore, the analysis of the saturated impulse based on the numerical simulations indicated that the saturation phenomenon was more likely to be achieved as the water impact velocity increased.