Stress and strain analysis in lifted segments has been a prominent subject, especially with the increasing use of thin plates and higher pre-outfitting rates, which has brought to light the previously overlooked issue of torsional deformation. This research focused on investigating the mechanics of torsional deformation in thin-plate open-section segments with nonuniform weight distributions during lifting. By simulating the effect of the pre-outfitting weight using offset weights, establishing reasonable lifting point constraints, and creating a typical segment lifting analysis model, the results obtained through nonlinear and linear finite element analyses were compared. Extensive data was employed to establish the relationship between the torsional deformation and offset weights, using the principle of energy and the linear regression method. This analysis uncovered substantial disparities between the linear and nonlinear methods, with the linear approach failing to provide accurate stress and strain for torsion. Furthermore, the stress distribution obtained from nonlinear finite element analysis adhered to contemporary torsional theory. This paper underscores the nonlinear nature of torsional deformation, elucidates the influence of the weight distribution, and establishes a functional relation between the torsional deformation and offset weights, offering a robust tool for optimizing outfitting design and construction schemes.

This study is aimed at reducing the occurrence of oil spill accidents in the engine room of ships and carries out risk prevention for the equipment of the port ships, thereby reducing pollution to the marine ecological environment. Firstly, the concepts and principles of cluster analysis and ship automatic identification system are expounded. Secondly, the data information collected by the ships automatic identification system (AIS) is combined with density-based cluster analysis. The accident area and extent at different stages of the ships engine room equipment are classified. Finally, cluster analysis is used to evaluate the risk of equipment of the port ship engine room. The results show that there are 43000 ship operation information points in port a, the average operating speed of ships is 9 kilometers, and the fastest operation speed is 16.9 kilometers. In addition, many ship routes in port a need to take risk prevention measures to minimize the impact between ships and reduce the risk of oil leakage in the engine room. The proposed cabin model can easily and quickly analyze the orientation information of the ship and classify all the data into different types according to the surrounding information points. AIS can realize the information transfer between ships and between ship and shore. Information such as the position, speed, and direction of the ship needs to be accurately known to ensure safety at sea. These data need to relate to some terminals and networks to form a maritime monitoring network. The ship AIS based on cluster analysis can cluster the areas where the ships speed and direction change significantly in the port area, effectively preventing accidents. Scientific risk prevention measures can effectively reduce the oil leakage risk of ship engine room equipment, improve the working efficiency of marine engines, and provide a strong foundation for the entire marine ecological environment protection.