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AuthorSenouci, Ahmed
AuthorElabbasy, Mohamed
AuthorElwakil, Emad
AuthorAbdrabou, Bassem
AuthorZayed, Tarek
Available date2016-03-24T13:39:02Z
Publication Date2014
Publication NameStructure and Infrastructure Engineering
ResourceScopus
CitationSenouci, A., Elabbasy, M., Elwakil, E., Abdrabou, B., Zayed, T. "A model for predicting failure of oil pipelines" (2014) Structure and Infrastructure Engineering, 10 (3), pp. 375-387.
ISSN1573-2479
URIhttp://dx.doi.org/10.1080/15732479.2012.756918
URIhttp://hdl.handle.net/10576/4263
AbstractOil and gas pipelines transport millions of dollars of goods everyday worldwide. Even though they are the safest way to transport petroleum products, pipelines do still fail generating hazardous consequences and irreparable environmental damages. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models were limited to one failure type, such as corrosion failure, or relied mainly on expert opinion analysis. The objective of this paper is to develop a model that predicts the failure cause of oil pipelines based on factors other than corrosion. Two models are developed to help decision makers predict failure occurrence. Regression analysis and artificial neural networks (ANNs) models were developed based on historical data of pipeline accidents. The two models were able to satisfactory predict pipeline failures due to mechanical, operational, corrosion, third party and natural hazards with an average validity of 90% for the regression model and 92% for the ANN model. The developed models assist decision makers and pipeline operators to predict the expected failure cause(s) and to take the necessary actions to avoid them.
SponsorQatar National Research Fund (QNRF) for this research project under award no. QNRF-NPRP 09-901-2-343.
Languageen
PublisherTaylor & Francis
Subjectartificial neural networks
Subjectfailure type prediction
Subjectoil pipelines
Subjectregression
TitleA model for predicting failure of oil pipelines
TypeArticle
Pagination375-387
Issue Number3
Volume Number10


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