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

• Published in: Ocean engineering 2024-11, Vol.311, p.118808, Article 118808
• Main Authors: Alrsai, Mahmoud, Alsahalen, Ala’, Karampour, Hassan, Alhawamdeh, Mohammad, Alajarmeh, Omar
• Format: Article
• Language: English
• Subjects: Buckle propagation; Collapse; Hybrid steel-CFRP pipe; Machine learning; U-shape failure
• ISSN: 0029-8018
• DOI: 10.1016/j.oceaneng.2024.118808

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 σys/σuc, 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. •Predicting PP in hybrid steel-CFRP pipes using FE and ML; model validated against prior experiments.•Failure modes identified; 3-phase ML framework with RF, KNN, GP, MLP, and SVM proposed for analysis.•GP, KNN, RF outperform SVM-GK, MLP, SVM-LK in PP prediction; feature importance also analyzed.•Combining ML with dimensional analysis enhances understanding of hybrid steel-CFRP pipe behavior.