Ship structure ice level strengthening method based on artificial intelligence

By combining artificial intelligence and structural strength standards, a structural ice-resistance level database was established. By using multi-input neural networks to generate ice-level reinforcement schemes, the problems of cumbersome design and reliance on manual experience in existing ship structure ice-level reinforcement methods were solved. This achieved automated and intelligent ice-level reinforcement design, improving safety and economy.

CN122065441APending Publication Date: 2026-05-19JIANGSU UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610179608.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-09
Publication Date
2026-05-19

AI Technical Summary

Technical Problem

Existing methods for strengthening ship structures by ice class are cumbersome in design, rely on manual experience, lack a unified and efficient calculation approach, and are difficult to reflect changes in ice load in real time, resulting in large differences in design results and making it difficult to guarantee safety and economy.

Method used

By combining artificial intelligence with structural strength standards, a database of structural ice resistance levels is established through finite element simulation modeling and deep learning. Ice-level reinforcement schemes are generated using multi-input neural networks, achieving automated and intelligent design.

Benefits of technology

It has achieved automation and intelligence in ice-level strengthening of ship structures, generated targeted strengthening schemes, improved the safety and economy of the design, and enhanced the consistency between ice load calculation and actual environment.

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Abstract

The invention discloses a ship structure ice level strengthening method based on artificial intelligence, which comprises the following steps of: acquiring basic parameter information of various types of ships, and performing finite element simulation modeling and calculation to obtain stress distribution, displacement and total strain of a ship body structure under the combination of various ship types and ice conditions; performing linear strength direct calculation or non-linear strength direct calculation on the obtained data, analyzing a potential failure mode of the ship, and forming a structure ice-resistant grade database in combination with evaluation of the structure ice-resistant grade; an artificial intelligence model is established, and the structural ice-resistant grade database is used for training; basic parameters of a target ship in actual navigation are input into the trained artificial intelligence model, and an ice-level strengthening scheme matched with the target ship is generated through feature extraction, rule reasoning and decision output. According to the method, automation and intellectualization of ice level strengthening design under different ship types and ice area levels are achieved, and the safety, economical efficiency and efficiency of polar navigation ship structure design are improved.
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