ArticleMaterials (Basel, Switzerland)2025
A Hybrid FE-ML Approach for Critical Buckling Moment Prediction in Dented Pipelines Under Complex Loadings.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Fitness-for-Service Assessment of Dent Defects on Steel Energy Pipelines: Evaluation Criteria, Integrity Prediction, and Future Challenges.Materials (Basel, Switzerland) · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
Dents are a common geometric deformation defect in pipelines where the dented section becomes susceptible to local buckling, significantly threatening the integrity and reliability of the pipeline. This paper developed a novel finite element (FE) machine learning (ML)-based approach to analyze and predict the critical buckling moment (CBM) of dented pipelines under combined internal pressure and bending moment (BM) loading. By quantifying the parametric effects on CBM and developing a dataset, an Extreme Learning Machine (ELM) framework through hybrid algorithm integration, combining Bald Eagle Search (BES), Lévy flight, and Simulated Annealing (SA), was proposed to achieve highly accurate CBM predictions. This study offers valuable insights into evaluating the buckling resistance of dented pipelines subjected to complex loading conditions.
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Registered trials
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