In the field of offshore engineering, the number of offshore platforms is increasing with the development of the offshore oil industry. During the working period, it is not only affected by seawater impact and pile foundation scouring, but also affected by marine debris, biological attachment, corrosion, etc., and extreme weather such as typhoons and earthquakes will also have an impact. Therefore, it is unavoidable during use. will be damaged. In order to ensure safety during platform services and effectively prevent serious accidents, it is necessary to conduct regular tests and safety assessments during platform services. In this paper, the offshore platform is taken as an example of numerical analysis, the ABAQUS finite element analysis software is used to establish the structural model of the marine platform, the structural parameters of the marine engineering are identified through the deep CNN(CNN), and the damage conditions of single-component damage and multi-component damage are simulated calculate. The results show that the CNN has anti-noise ability for damage assessment of marine engineering structural parameters, completes the localization and quantitative solution of damage, and achieves a certain DD(DD) effect.

Jumsha Khanen. Parameter Identification and Damage Diagnosis of Marine Engineering Structures Based on Deep Learning. Frontiers in Ocean Engineering (2020), Vol. 1, Issue 3: 44-51. https://doi.org/10.38007/FOE.2020.010306.

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