1. A simulation-based and data-augmented shear force inversion method for offshore platform connector.
- Author
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Zhang, Tao, Oterkus, Selda, Oterkus, Erkan, Wang, Xueliang, Wang, Fang, and Shiqian, Song
- Subjects
- *
SHEARING force , *ARTIFICIAL neural networks - Abstract
This study introduces a Simulation-Based and Data-Augmented method for shear force inversion to address the challenge of directly measuring shear force on connector pins in multi-module floating platforms. Stress sensors are strategically placed in adjacent areas. Extensive Finite Element simulation scenarios lead to the identification of optimal features sensitive to both force magnitude and direction. Subsequently, an Artificial Neural Network (ANN) is developed to distill the simulation data into characteristic sensor responses. Fine-tuning with physical measurements further enhances shear force inversion accuracy. Using simulated and experimental data, the method demonstrates a shear force inversion error below 3.2 % and an angular inversion error under 1.4 % across test conditions. This methodology provides essential load data for connector safety assessments and crucial guidelines for the assembly of multi-module floating platforms. • A simulation-based and data-augmented method for shear force inversion in offshore platform connectors. • The artificial neural networks (ANN) incorporate feature point selection through finite element theory and coefficient correlation. • The generalization performance of the ANN is evaluated through simulated and structural test data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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