Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-CFRP subsea pipelines

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@Article{Alrsai:2024:oceaneng,

• author = "Mahmoud Alrsai and Ala' Alsahalen and Hassan Karampour and Mohammad Alhawamdeh and Omar Alajarmeh",
• title = "Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-{CFRP} subsea pipelines",
• journal = "Ocean Engineering",
• year = "2024",
• volume = "311",
• pages = "118808",
• keywords = "genetic algorithms, genetic programming, Hybrid steel-CFRP pipe, Buckle propagation, U-shape failure, Collapse, Machine learning",
• ISSN = "0029-8018",
• URL = "https://www.sciencedirect.com/science/article/pii/S0029801824021462",
• DOI = "doi:10.1016/j.oceaneng.2024.118808",
• abstract = "Accurate prediction of the propagation pressure (PP) in hybrid steel-CFRP pipe systems presents a substantial challenge due to intricate interactions and complex collapse failure modes. An efficient FE-based algorithm is programmed using ANSYS to numerically estimate the PP of hybrid steel-CFRP pipe, subjected to external pressure. This study employs a machine learning (ML) framework, addressing the inherent complexity with a three-phase approach: Parameter Design, Buckle Propagation Analysis, and ML Model Development. The dataset, encompassing about two thousand observations with four key features, undergoes k-fold cross-validation and min-max normalization for robust ML performance. Five ML models-Random Forest (RF), K-Nearest Neighbors (KNN), Genetic Programming (GP), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM)-are developed and evaluated. The results revealed a significant influence of Ds/ts, a three-phase relationship with ts/tc, and a substantial decrease in PPh/PPs with increasing sigmays/sigmauc, predominantly exhibiting U-shaped or dog-bone failure modes in different scenarios. Proven that GP, KNN, and RF are the superior performers, ranking ahead of SVM with Gaussian Kernel (SVM-GK), MLP, and SVM with Linear Kernel (SVM-LK). Statistical metrics, Taylor Diagram analysis, and comparisons with FE results emphasize the effectiveness of GP, KNN, and RF. Additionally, normality tests and feature importance analysis provide nuanced insights",

}

Genetic Programming entries for Mahmoud Alrsai Ala' Alsahalen Hassan Karampour Mohammad Alhawamdeh Omar Alajarmeh

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