Volume 5: Materials &Amp; Fabrication 2023
DOI: 10.1115/pvp2023-106471
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Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels

William R. Johnson,
Xian-Kui Zhu,
Robert Sindelar
et al.

Abstract: The common use of pressure vessels (PVs) and pipelines and the high cost of their failure or overdesign in the oil and gas industry makes predicting their burst pressure critical. However, experimental tests of burst pressure are expensive and finite element analysis (FEA) is time consuming. Artificial neural networks (ANNs) hold the promise of rapid prediction of burst pressure for a wide range of PV materials and geometries, including pipelines with defects such as corrosion. This paper demonstrates ANNs des… Show more

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