Prediction of Fracture Stresses of High Pressure Gas Cylinders
                    Containing Cracklike Flaws

Rana, Mahendra; Rawls, George B.

doi:10.1115/1.2767346

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…A total of 250 full-scale tests of pipe specimens containing single longitudinal surface cracks are collected from the literature [2,20,31,36,40,[57][58][59]63]. The test data include 135 leaks and 115 ruptures.…”

Section: Full-scale Burst Tests Of Pipes Containing Longitudinal Surf...mentioning

confidence: 99%

“…The CorLAS-S model is inapplicable to three tests in [40] because the ultimate tensile strengths of the specimens are not provided. Solutions of σ hb were not obtained due to numerical difficulties when the CorLAS-S model was applied to the six tests reported in [57] and thirteen tests reported in [58]. The predictive performance of the mechanics-based approach corresponding to the two scenarios is presented in Fig.…”

Section: Predictions Of the Mechanics-based Approach Based On Test Datamentioning

confidence: 99%

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Classification of failure modes of pipelines containing longitudinal surface cracks using mechanics-based and machine learning models

Sun

Zhou

2023

J Infrastruct Preserv Resil

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This paper applies the mechanics-based approach and five machine learning algorithms to classify the failure mode (leak or rupture) of steel oil and gas pipelines containing longitudinally oriented surface cracks. The mechanics-based approach compares the nominal hoop stress remote from the surface crack at failure and the remote nominal hoop stress to cause unstable longitudinal propagation of the through-wall crack to predict the failure mode. The employed machine learning algorithms consist of three single learning algorithms, namely naïve Bayes, support vector machine and decision tree; and two ensemble learning algorithms, namely random forest and gradient boosting. The classification accuracy of the mechanics-based approach and machine learning algorithms is evaluated based on 250 full-scale burst tests of pipe specimens collected from the open literature. The analysis results reveal that the mechanics-based approach leads to highly biased classifications: many leaks erroneously classified as ruptures. The machine learning algorithms lead to markedly improved accuracy. The random forest and gradient boosting models result in the classification accuracy of over 95% for ruptures and leaks, with the accuracy of the decision tree and support vector machine models somewhat lower. This study demonstrates the value of employing machine learning models to improve the integrity management practice of oil and gas pipelines.

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Section: Full-scale Burst Tests Of Pipes Containing Longitudinal Surf...mentioning

confidence: 99%

Section: Predictions Of the Mechanics-based Approach Based On Test Datamentioning

confidence: 99%

Classification of failure modes of pipelines containing longitudinal surface cracks using mechanics-based and machine learning models

Sun

Zhou

2023

J Infrastruct Preserv Resil

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An updated continuum damage model to investigate fracture process of structures in DBTT region

Wang

Shi

et al. 2008

Int J Fract

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In the Ductile-Brittle Transition Temperature (DBTT) region, it is not realistic to take unique fracture stress or fracture strain as the fracture criterion to investigate the fracture properties. In this paper, an updated continuum damage model was proposed, in which the fracture energy density, a function of the stress triaxiality, temperature and strain rate in the transition region was taken as the critical damage factor. Uniaxial tension tests were carried out to get the basic material properties at different temperatures, to calibrate the fracture model constants and verify the validity of the damage model. The fracture behaviour of pipes with penetrating cracks under the internal pressure was experimentally investigated with the load-deflection curves and the crack propagation length captured from tests. The J -R curves were obtained from the testing results for different temperatures. Based on the Finite Element Analyses (FEA) with the proposed fracture criterion of the updated continuum damage model, the loading level of pipes with penetrating cracks were estimated and compared with the experimental results. Meanwhile the fracture processes of the pipeline structures in the transition region were reproduced. The experimental and numerical results agreed very well in present calculations. It has been shown that the fracture process in the transition region strongly depends on both the stress and strain states, and could be effectively predicted using the continuum damage model.

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Model error assessment of burst capacity models for energy pipelines containing surface cracks

Yan¹,

Zhang²,

Zhou³

2014

International Journal of Pressure Vessels and Piping

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Prediction of Fracture Stresses of High Pressure Gas Cylinders Containing Cracklike Flaws

Cited by 7 publications

References 2 publications

Classification of failure modes of pipelines containing longitudinal surface cracks using mechanics-based and machine learning models

Classification of failure modes of pipelines containing longitudinal surface cracks using mechanics-based and machine learning models

An updated continuum damage model to investigate fracture process of structures in DBTT region

Model error assessment of burst capacity models for energy pipelines containing surface cracks

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