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AuthorEl-Abbasy, M.
AuthorSenouci, A.
AuthorZayed, T.
AuthorMirahadi, F.
AuthorParvizsedghy, L.
Available date2016-03-06T14:12:56Z
Publication Date2014-06
Publication NameJournal of Construction Engineering and Management
ResourceScopus
CitationEl-Abbasy, M., Senouci, A., Zayed, T., Mirahadi, F., and Parvizsedghy, L. (2014), "Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis", Journal of Construction Engineering Management.
ISSN0733-9364
URIhttp://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000838
URIhttp://hdl.handle.net/10576/4198
AbstractAlthough they are the safest means of transporting oil and gas products, pipelines can sometimes fail with hazardous consequences and large business losses. The decision to replace, repair, or rehabilitate depends mainly on the condition of the pipeline. Assessing and predicting its condition is therefore a key step in the maintenance plan of a pipeline. Several models have recently been developed to predict pipeline failures and conditions. However, most of these models were limited to the use of corrosion as the sole factor to assess the condition of pipelines. The objective of this paper is to develop models that assess and predict the condition of oil and gas pipelines based on several factors including corrosion. The regression analysis technique was used to develop the condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on expert opinion. The models were able to satisfactorily predict pipeline condition with an average percent validity above 96% when applied to the validation data set. The models are expected to help decision makers assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspection and rehabilitation planning.
SponsorQatar National Research Fund (QNRF) for this research project under award No. QNRF-NPRP 09-901-2-343
Languageen
PublisherAmerican Society of Civil Engineers (ASCE)
SubjectCondition prediction
Oil and gas pipelines
Quantitative methods
Regression analysis
TitleCondition prediction models for oil and gas pipelines using regression analysis
TypeArticle
Issue Number6
Volume Number140
dc.accessType Abstract Only


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