FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4229014774> ?p ?o ?g. }

• W4229014774 endingPage "111382" @default.
• W4229014774 startingPage "111382" @default.
• W4229014774 abstract "This paper aims to predict the failure pressure of corroded offshore pipelines, employing different machine learning techniques. To this end, an efficient finite element based algorithm is programmed to numerically estimate the failure pressure of offshore pipelines, subjected to internal corrosion. In this process, since the computational effort of such numerical assessment is very high, the application of reliable machine learning methods is used as an alternative solution. Thus, 1815 realizations of four variables are generated, and each one is keyed into the numerical model of a sample pipeline. Thereafter, the machine learning models are constructed based on the results of the numerical analyses, and their performance are compared with each other. The results indicate that Gaussian Process Regression (GPR) and MultiLayer Perceptron (MLP) have the best performance among all the chosen models. Considering the testing dataset, the squared correlation coefficient and Root Mean Squared Error (RMSE) values of GPR and MLP models are 0.535, 0.545 and 0.993 and 0.992, respectively. Moreover, the Maximum Von-Mises Stress (MVMS) of the pipeline increases as the water depth grows at low levels of Internal Pressure (IP). Inversely, increase in water depth leads to reduction in the MVMS values at high IP levels. • An internally corroded offshore pipeline has been numerically modeled. • A finite element based method is used to find the failure pressure of pipelines. • 6 ML methods are used to predict the failure pressure of the corroded pipelines. • The effects of external and internal pressure are studied on pipelines' failure." @default.
• W4229014774 created "2022-05-08" @default.
• W4229014774 creator A5008678573 @default.
• W4229014774 creator A5022562863 @default.
• W4229014774 creator A5024057308 @default.
• W4229014774 creator A5054242967 @default.
• W4229014774 date "2022-06-01" @default.
• W4229014774 modified "2023-10-17" @default.
• W4229014774 title "Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques" @default.
• W4229014774 cites W1894025508 @default.
• W4229014774 cites W1964357740 @default.
• W4229014774 cites W1967263382 @default.
• W4229014774 cites W1967938232 @default.
• W4229014774 cites W1977664222 @default.
• W4229014774 cites W1985479415 @default.
• W4229014774 cites W1987120052 @default.
• W4229014774 cites W1990441304 @default.
• W4229014774 cites W2005096544 @default.
• W4229014774 cites W2040775834 @default.
• W4229014774 cites W2043435847 @default.
• W4229014774 cites W2044163646 @default.
• W4229014774 cites W2054453623 @default.
• W4229014774 cites W2082939027 @default.
• W4229014774 cites W2138763184 @default.
• W4229014774 cites W2195613835 @default.
• W4229014774 cites W2216769207 @default.
• W4229014774 cites W2301631735 @default.
• W4229014774 cites W2343688764 @default.
• W4229014774 cites W2404860312 @default.
• W4229014774 cites W2500485441 @default.
• W4229014774 cites W2620277952 @default.
• W4229014774 cites W2729747888 @default.
• W4229014774 cites W2763309497 @default.
• W4229014774 cites W2766110825 @default.
• W4229014774 cites W2769251138 @default.
• W4229014774 cites W2791336691 @default.
• W4229014774 cites W2795223845 @default.
• W4229014774 cites W2906566035 @default.
• W4229014774 cites W2910140101 @default.
• W4229014774 cites W2910991118 @default.
• W4229014774 cites W2911964244 @default.
• W4229014774 cites W2930890426 @default.
• W4229014774 cites W2954648193 @default.
• W4229014774 cites W2976698016 @default.
• W4229014774 cites W2987128843 @default.
• W4229014774 cites W3001792270 @default.
• W4229014774 cites W3011376272 @default.
• W4229014774 cites W3022856716 @default.
• W4229014774 cites W3066846904 @default.
• W4229014774 cites W3080590579 @default.
• W4229014774 cites W3083392807 @default.
• W4229014774 cites W3083858902 @default.
• W4229014774 cites W3091232902 @default.
• W4229014774 cites W3152326459 @default.
• W4229014774 cites W3169393435 @default.
• W4229014774 cites W3169481187 @default.
• W4229014774 cites W3169647254 @default.
• W4229014774 cites W4212883601 @default.
• W4229014774 cites W4251428492 @default.
• W4229014774 cites W977828450 @default.
• W4229014774 doi "https://doi.org/10.1016/j.oceaneng.2022.111382" @default.
• W4229014774 hasPublicationYear "2022" @default.
• W4229014774 type Work @default.
• W4229014774 citedByCount "8" @default.
• W4229014774 countsByYear W42290147742022 @default.
• W4229014774 countsByYear W42290147742023 @default.
• W4229014774 crossrefType "journal-article" @default.
• W4229014774 hasAuthorship W4229014774A5008678573 @default.
• W4229014774 hasAuthorship W4229014774A5022562863 @default.
• W4229014774 hasAuthorship W4229014774A5024057308 @default.
• W4229014774 hasAuthorship W4229014774A5054242967 @default.
• W4229014774 hasConcept C11413529 @default.
• W4229014774 hasConcept C119857082 @default.
• W4229014774 hasConcept C127413603 @default.
• W4229014774 hasConcept C135628077 @default.
• W4229014774 hasConcept C159985019 @default.
• W4229014774 hasConcept C162284963 @default.
• W4229014774 hasConcept C175309249 @default.
• W4229014774 hasConcept C179717631 @default.
• W4229014774 hasConcept C187320778 @default.
• W4229014774 hasConcept C18747710 @default.
• W4229014774 hasConcept C192562407 @default.
• W4229014774 hasConcept C199104240 @default.
• W4229014774 hasConcept C2780092901 @default.
• W4229014774 hasConcept C41008148 @default.
• W4229014774 hasConcept C43521106 @default.
• W4229014774 hasConcept C50644808 @default.
• W4229014774 hasConcept C66938386 @default.
• W4229014774 hasConcept C78519656 @default.
• W4229014774 hasConcept C81692654 @default.
• W4229014774 hasConceptScore W4229014774C11413529 @default.
• W4229014774 hasConceptScore W4229014774C119857082 @default.
• W4229014774 hasConceptScore W4229014774C127413603 @default.
• W4229014774 hasConceptScore W4229014774C135628077 @default.
• W4229014774 hasConceptScore W4229014774C159985019 @default.
• W4229014774 hasConceptScore W4229014774C162284963 @default.
• W4229014774 hasConceptScore W4229014774C175309249 @default.
• W4229014774 hasConceptScore W4229014774C179717631 @default.

• next