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 ID | 4710 |
| Article Category | Article |
| Authors | Alrsai, Mahmoud, Alsahalen, Ala’, Karampour, Hassan, Alhawamdeh, Mohammad and Alajrmeh, Omar |
| Journal Title | Ocean Engineering |
| Journal Citation | 311 (Part 1) |
| Article Number | 118808 |
| Number of Pages | 22 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0029-8018 |
| 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 |
| Keywords | Buckle propagation; Hybrid steel-CFRP pipe; U-shape failure; Collapse; Machine learning |
| Related Output | |
| Is supplemented by | Corrigendum “Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid Steel-CFRP subsea pipelines” [Ocean Eng. 311 (1) (2024) 118808] |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400510. Structural engineering |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
This article has been corrected. Please see the Related Output. | |
| Byline Affiliations | Al-Hussein Bin Talal University, Jordan |
| Independent Researcher, Jordan | |
| Griffith University | |
| Tafila Technical University, Jordan | |
| Centre for Future Materials |
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https://research.usq.edu.au/item/z9983/integrated-finite-element-analysis-and-machine-learning-approach-for-propagation-pressure-prediction-in-hybrid-steel-cfrp-subsea-pipelines
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