Failure pressure prediction of corroded pipes under combined internal pressure and axial compressive force

Bruère, Vivianne Marie; Bouchonneau, Nadège; Motta, Renato de Siqueira; Afonso, Silvana Maria Bastos; Willmersdorf, Ramiro Brito; Lyra, Paulo Roberto Maciel; Torres, Juliana Von Schmalz; Andrade, Edmundo Q. de; Cunha, Divino J. S.

doi:10.1007/s40430-019-1674-2

Cited by 20 publications

(5 citation statements)

References 14 publications

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“…A proprietary program, PIPEFLAW, is able to quickly assess the integrity of corroded pipelines. The programme is considered a Level 4 assessment method as it can automatically generate and analyse corroded pipeline models subjected to internal pressure and axial compressive force [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Level 3 assessment methods involve advance analysis techniques and require a higher level of information such as boundary conditions and material properties for the analysis. Numerical methods such as the finite element method (FEM) have been widely employed to verify and validate the failure behaviour and failure pressure of corroded pipelines [17,[19][20][21]. FEM is especially useful for parametric studies with varying corrosion geometries, which otherwise would be too costly to be carried out experimentally.…”

Section: Introductionmentioning

confidence: 99%

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Failure Pressure Prediction of a Corroded Pipeline with Longitudinally Interacting Corrosion Defects Subjected to Combined Loadings Using FEM and ANN

Karuppanan

Ovinis

2021

JMSE

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Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from −9.39% to 4.63%, when compared with FEA results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Failure Pressure Prediction of a Corroded Pipeline with Longitudinally Interacting Corrosion Defects Subjected to Combined Loadings Using FEM and ANN

Karuppanan

Ovinis

2021

JMSE

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“…Heitzer 35 presented a mathematical programming formulation and used a numerical procedure to analyze pipeline plastic collapse under internal pressure and axial force. Benjamin 36 and Bruère et al 37 proposed an approach to assess failure pressure that considers the impact of axial compressive force. Arumugam et al 38 utilized the FE method to determine the bearing capacity of colony corrosion defects under compressive load and internal pressure.…”

Section: Introductionmentioning

confidence: 99%

Limit state equation and failure pressure prediction model of pipeline with complex loading

Sun,

Fang,

Wang

et al. 2024

Nat Commun

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Assessing failure pressure is critical in determining pipeline integrity. Current research primarily concerns the buckling performance of pressurized pipelines subjected to a bending load or axial compression force, with some also looking at the failure pressure of corroded pipelines. However, there is currently a lack of limit state models for pressurized pipelines with bending moments and axial forces. In this study, based on the unified yield criterion, we propose a limit state equation for steel pipes under various loads. The most common operating loads on buried pipelines are bending moment, internal pressure, and axial force. The proposed limit state equation for intact pipelines is based on a three-dimensional pipeline stress model with complex load coupling. Using failure data, we investigate the applicability of various yield criteria in assessing the failure pressure of pipelines with complex loads. We show that the evaluation model can be effectively used as a theoretical solution for assessing the failure pressure in such circumstances and for selecting appropriate yield criteria based on load condition differences.

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“…However, burst tests are expensive and time consuming. Numerical methods such as the finite element method (FEM) are widely used to verify and validate the failure behaviour and remaining strength of corroded pipelines [11][12][13][14][15]. These methods are especially useful for parametric studies with varying corrosion geometries that would otherwise will be too costly to be experimentally carried out.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

Kumar

Karuppanan

et al. 2022

Applied Sciences

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Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API 5L X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged from −6.33% to 2.39% when compared to an unseen FEA dataset, with a correlation value (R2) of 0.9975. A parametric study was subsequently performed using the equation to determine the effects of material property, defect depth, defect length, and longitudinal compressive stress on the remaining strength of pipelines with a single corrosion defect. Defect depth reduced the failure pressure by more than 65% on average, longitudinal compressive stress by more than 20% on average, and defect length by more than 21% on average.

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Failure pressure prediction of corroded pipes under combined internal pressure and axial compressive force

Cited by 20 publications

References 14 publications

Failure Pressure Prediction of a Corroded Pipeline with Longitudinally Interacting Corrosion Defects Subjected to Combined Loadings Using FEM and ANN

Failure Pressure Prediction of a Corroded Pipeline with Longitudinally Interacting Corrosion Defects Subjected to Combined Loadings Using FEM and ANN

Limit state equation and failure pressure prediction model of pipeline with complex loading

An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

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