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Machine Learning Models Development to Predict Corroded Pipeline Behavior Considering Defects Interaction
Soheyl Hosseinzadeh *1 , Mohammad Reza Bahaari1 , Mohsen Abayni1
1- University of Tehran
Abstract:   (25 Views)
Internal corrosion poses a significant risk to offshore pipeline operations. This study aims to utilize a combination of the Finite Element Method (FEM) and Latin Hypercube Sampling (LHS) to create a database of structural response data for corroded pipelines experiencing longitudinally interacting internal corrosion defects under internal and external pressure loading. The database includes input data such as pipeline geometry parameters, pipeline material data, corrosion defect data and loading data. This generated database will be utilized to train various advanced machine learning (ML) models to develop a predictive model capable of estimating the Maximum von Mises Stress occurring in the outermost mesh layer of a mesh ligament within the thickness of the corroded pipeline at the defected area. Such predictive capabilities of the ML model will enhance the ability to forecast leakage based on pipeline and defect specifications, thereby saving costs and time. To achieve the optimal model, various ML algorithms have been compared. Finally, to assess the prediction accuracy of the models, results of models were compared and evaluated.
 
Keywords: Offshore Pipeline Engineering, Pipeline Integrity Management, Structural Reliability, Random Sampling, Latin Hypercube Sampling, Machine Learning
Full-Text [PDF 1296 kb]   (14 Downloads)    

Highlights 
  • Developed LHS-FEM database to predict MVMS in corroded pipelines using ML.
  • Integrated ABAQUS with Python to automate nonlinear FE analysss for pipeline defects.
  • Evaluated five ML models with grid search and k-fold to optimize prediction accuracy.
  • NN and DTR models performed well in predicting MVMS; NN showed robust generalization.
  • Used learning curve analysis and PI method to validate models and assess parameters.

Type of Study: Research Paper | Subject: Offshore Structure
Received: 2025/08/19 | Accepted: 2025/10/8
Audio file of the article abstract [M4A 4495 KB]  (3 Download)
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