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

Article


Alrsai, Mahmoud, Alsahalen, Ala’, Karampour, Hassan, Alhawamdeh, Mohammad and Alajrmeh, Omar. 2024. "Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-CFRP subsea pipelines." Ocean Engineering. 311 (Part 1). https://doi.org/10.1016/j.oceaneng.2024.118808
Article Title

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

ERA Journal ID4710
Article CategoryArticle
AuthorsAlrsai, Mahmoud, Alsahalen, Ala’, Karampour, Hassan, Alhawamdeh, Mohammad and Alajrmeh, Omar
Journal TitleOcean Engineering
Journal Citation311 (Part 1)
Article Number118808
Number of Pages22
Year2024
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0029-8018
1873-5258
Digital Object Identifier (DOI)https://doi.org/10.1016/j.oceaneng.2024.118808
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0029801824021462
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 ?ys/?uc, 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. © 2024 Elsevier Ltd
KeywordsBuckle propagation; Hybrid steel-CFRP pipe; U-shape failure; Collapse; Machine learning
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Is supplemented byCorrigendum “Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-CFRP subsea pipelines” [Ocean Eng. 311 (1) (2024) 118808]
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ANZSRC Field of Research 2020400510. Structural engineering
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Byline AffiliationsAl-Hussein Bin Talal University, Jordan
Independent Researcher, Jordan
Griffith University
Tafila Technical University, Jordan
Centre for Future Materials
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