The repair of dents in oil or gas pipelines is mandated based on depth and interaction with stress risers, according to pipeline regulations in Canada and the United States. However, there have been cases where dents that did not meet the regulatory repair criteria have ended up failing, leading to operator need for an accurate assessment method for dents in order to maintain safety. While there is no agreed-upon method currently available in industry, conservative techniques employed by operators have led to poor dig efficiency. Recent research in industry has focused on strain- and fatigue-based techniques to assess the severity of dents and prioritize them for excavation and repair. Finite element analysis has been highlighted as an accurate method to evaluate strains and stresses within dented regions of pipe, although the significant computational time required for this method makes it inefficient for system-wide analysis. In this paper, the results from hundreds of finite element analysis models are used to train artificial neural networks. Subsequently, the artificial neural networks output accurate stresses and strains, that would be obtained using finite element analysis, when presented with input dent and pipe information. As a result, the artificial neural networks harness the accurate results that can be obtained from finite element analysis while results can be obtained efficiently for applicability to a pipeline system.
How to Cite
Pipelines, dents, finite element analysis, artificial neural networks, pipeline integrity, deformation.
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