Journal ArticleDOI
Corroded pipeline failure analysis using artificial neural network scheme
TLDR
The failure behavior of pipelines with interacting corrosion defects was studied using a finite element method, and a solution was proposed to predict burst pressure using an artificial neural network to prove its applicability and efficiency.About:
This article is published in Advances in Engineering Software.The article was published on 2017-10-01. It has received 74 citations till now.read more
Citations
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Journal ArticleDOI
Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment
TL;DR: In this paper, a comprehensive review of the pipeline corrosion assessment with the magnetic-flux leakage (MFL) technique from the data analytic perspective is provided, where the analyses of the MFL signal and data contribute to both corrosion quantification and prediction.
Journal ArticleDOI
Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
Afzal Ahmed Soomro,Ainul Akmar Mokhtar,Jundika Chandra Kurnia,Najeebullah Lashari,Najeebullah Lashari,Huimin Lu,Chico Sambo +6 more
TL;DR: It was found that most current research utilizes field data, simulation data, and experimental data, with field data being the most often used, and ANN, SVM, and hybrid models outperform due to the combined strength of the constituent models.
Journal ArticleDOI
Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
TL;DR: A recent systematic review of machine learning-based integrity assessments of corroded oil and gas pipelines is presented in this paper , which provides an overview of the most frequently used machine learning models for corroded pipeline integrity evaluation.
Journal ArticleDOI
An optimization of artificial neural network modeling methodology for the reliability assessment of corroding natural gas pipelines
TL;DR: The optimized model is applied to the reliability assessment of a corroded pipe with two successive inline inspections and according to the physical parameters of the pipeline, the trend of corroding pipeline reliability in time is predicted.
References
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Journal ArticleDOI
Reliability of pipelines with corrosion defects
TL;DR: In this article, the reliability of pipelines with corrosion defects subjected to internal pressure using the first-order reliability method (FORM) is defined based on the results of a series of small-scale experiments and three-dimensional nonlinear finite element analysis of the burst pressure of intact and corroded pipelines.
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The effect of corrosion defects on the burst pressure of pipelines
TL;DR: In this paper, the effect of external corrosion defects was studied via a series of small-scale experiments and through a nonlinear numerical model based on the finite element method, which was used to determine the burst pressure as a function of material and geometric parameters of different pipes and defects.
Journal ArticleDOI
Artificial neural network models for predicting condition of offshore oil and gas pipelines
TL;DR: In this paper, the authors present the development of models that evaluate and predict the condition of offshore oil and gas pipelines based on several factors besides corrosion, such as historical inspection data collected from three existing offshore pipelines in Qatar.
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Development of limit load solutions for corroded gas pipelines
TL;DR: In this article, a fitness-for-purpose (FFP) type limit load solution for corroded gas pipelines made of X65 steel is proposed and a series of burst tests with various types of machined pits are performed.
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Ductile failure analysis of API X65 pipes with notch-type defects using a local fracture criterion
TL;DR: In this article, a local failure criterion was proposed to predict ductile failure of full-scale API X65 pipes with simulated corrosion and gouge defects under internal pressure, based on detailed finite element (FE) analyses with the proposed local fracture criterion, burst pressures of defective pipes were estimated and compared with experimental data.