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.