A Hybrid FE-ML Approach for Critical Buckling Moment Prediction in Dented Pipelines Under Complex Loadings
Year: 2025
Type: article
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.
Source: Materials
DOI: 10.3390/ma18204721
Authors: Yunfei Huang, Jianrong Tang, Dong Lin, Mingnan Sun, Jie Shu, Wei Liu, Xiangqin Hou
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: PetroChina Company Limited
OpenAlex ID: W4415206903