FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297916564> ?p ?o ?g. }

• W4297916564 abstract "Abstract Oil and gas pipeline failure and leakage can seriously damage people's lives and the ecosystem. The prediction of failure pressure for pipelines with damage is one of the most important and challenging tasks faced by industry, which affects the assessment of pipeline safety. Previous studies widely used industrial models or the finite element (FE) method to predict the failure pressure. However, the industrial models may give limited information, and the FE method has much heavy computation burden. In this work, three machine learning models - artificial neural network (ANN), XGBoost (XGB) and CatBoost (CAT) are developed for forecasting the failure pressure of pipelines with defects. Firstly, the simulation results of the FE method are validated by real failure pressure and compared with the calculation results of industrial models (ASME-B31G and DNV). Then 180 pipeline samples including pipeline attributes and defect sizes collected from real in-line inspection data in a pipeline company and the corresponding FE simulation results of failure pressure of these 180 defective pipelines are used for the training and testing of the machine learning models. The results show that the simulation accuracy of the FE method is higher than the calculation accuracy of the industrial models, and the FE simulation results are suitable to be the outputs of machine learning models. The three machine learning methods all provide satisfactory prediction accuracy in failure pressure. Specifically, CAT is the best machine learning method in this study for its lowest relative error (3.11% on average), mean absolute error (0.53), root mean square error (0.78) and highest coefficient of determination (R2) up to 98% in testing. Moreover, the machine learning models present better performance on average relative errors compared to the industrial models. CAT shows higher accuracy than the industrial models and FE simulation on minimum and average relative errors. Finally, the prediction result of CAT is used to discuss the effect of input features on failure pressure of pipelines, which demonstrates that the importance of features follows the order of pipeline thickness &gt; pipeline outside diameter &gt; defect depth &gt; defect length &gt; defect width. Once the above machine learning methods are used in industry, more and more real data will be collected to train a model and make it more accurate. In this way, these methods will provide an efficient way to evaluate the safety of defective pipelines. In addition, the failure pressure of pipeline could be estimated to help operators figure out a pipeline condition and further prioritize the pipelines for maintenance." @default.
• W4297916564 created "2022-10-01" @default.
• W4297916564 creator A5045453273 @default.
• W4297916564 creator A5071037763 @default.
• W4297916564 creator A5086480330 @default.
• W4297916564 date "2022-09-26" @default.
• W4297916564 modified "2023-10-18" @default.
• W4297916564 title "Failure Pressure Prediction of Defective Pipeline Using Finite Element Method and Machine Learning Models" @default.
• W4297916564 cites W1991901476 @default.
• W4297916564 cites W2077036197 @default.
• W4297916564 cites W2534043874 @default.
• W4297916564 cites W2556507341 @default.
• W4297916564 cites W2735615593 @default.
• W4297916564 cites W2740772203 @default.
• W4297916564 cites W2901048805 @default.
• W4297916564 cites W2985313557 @default.
• W4297916564 cites W2996325904 @default.
• W4297916564 cites W3005150217 @default.
• W4297916564 cites W3037657209 @default.
• W4297916564 cites W3134905447 @default.
• W4297916564 cites W3188517841 @default.
• W4297916564 cites W3198669675 @default.
• W4297916564 doi "https://doi.org/10.2118/210406-ms" @default.
• W4297916564 hasPublicationYear "2022" @default.
• W4297916564 type Work @default.
• W4297916564 citedByCount "0" @default.
• W4297916564 crossrefType "proceedings-article" @default.
• W4297916564 hasAuthorship W4297916564A5045453273 @default.
• W4297916564 hasAuthorship W4297916564A5071037763 @default.
• W4297916564 hasAuthorship W4297916564A5086480330 @default.
• W4297916564 hasConcept C11413529 @default.
• W4297916564 hasConcept C119857082 @default.
• W4297916564 hasConcept C122383733 @default.
• W4297916564 hasConcept C127413603 @default.
• W4297916564 hasConcept C135628077 @default.
• W4297916564 hasConcept C154945302 @default.
• W4297916564 hasConcept C175309249 @default.
• W4297916564 hasConcept C200601418 @default.
• W4297916564 hasConcept C41008148 @default.
• W4297916564 hasConcept C43521106 @default.
• W4297916564 hasConcept C45374587 @default.
• W4297916564 hasConcept C50644808 @default.
• W4297916564 hasConcept C66283442 @default.
• W4297916564 hasConcept C66938386 @default.
• W4297916564 hasConcept C78519656 @default.
• W4297916564 hasConceptScore W4297916564C11413529 @default.
• W4297916564 hasConceptScore W4297916564C119857082 @default.
• W4297916564 hasConceptScore W4297916564C122383733 @default.
• W4297916564 hasConceptScore W4297916564C127413603 @default.
• W4297916564 hasConceptScore W4297916564C135628077 @default.
• W4297916564 hasConceptScore W4297916564C154945302 @default.
• W4297916564 hasConceptScore W4297916564C175309249 @default.
• W4297916564 hasConceptScore W4297916564C200601418 @default.
• W4297916564 hasConceptScore W4297916564C41008148 @default.
• W4297916564 hasConceptScore W4297916564C43521106 @default.
• W4297916564 hasConceptScore W4297916564C45374587 @default.
• W4297916564 hasConceptScore W4297916564C50644808 @default.
• W4297916564 hasConceptScore W4297916564C66283442 @default.
• W4297916564 hasConceptScore W4297916564C66938386 @default.
• W4297916564 hasConceptScore W4297916564C78519656 @default.
• W4297916564 hasLocation W42979165641 @default.
• W4297916564 hasOpenAccess W4297916564 @default.
• W4297916564 hasPrimaryLocation W42979165641 @default.
• W4297916564 hasRelatedWork W1572523360 @default.
• W4297916564 hasRelatedWork W1603000436 @default.
• W4297916564 hasRelatedWork W2067716082 @default.
• W4297916564 hasRelatedWork W2347572307 @default.
• W4297916564 hasRelatedWork W2359870321 @default.
• W4297916564 hasRelatedWork W2528279091 @default.
• W4297916564 hasRelatedWork W2530037178 @default.
• W4297916564 hasRelatedWork W2948568013 @default.
• W4297916564 hasRelatedWork W4293056109 @default.
• W4297916564 hasRelatedWork W4297916564 @default.
• W4297916564 isParatext "false" @default.
• W4297916564 isRetracted "false" @default.
• W4297916564 workType "article" @default.