# A Hybrid FE-ML Approach for Critical Buckling Moment Prediction in Dented Pipelines Under Complex Loadings

## TL;DR

This paper introduces a new method combining finite element analysis and machine learning to predict buckling in dented pipelines under complex loads.

## Contribution

A novel hybrid FE-ML approach using ELM with BES, Lévy flight, and SA for accurate critical buckling moment prediction in dented pipelines.

## Key findings

- The hybrid algorithm integration achieves highly accurate CBM predictions.
- Parametric effects on CBM are quantified using a dataset developed for dented pipelines.
- The approach evaluates buckling resistance under combined internal pressure and bending moment loading.

## 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.