Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques
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- @Article{ABYANI:2022:oceaneng,
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author = "Mohsen Abyani and Mohammad Reza Bahaari and
Mohamad Zarrin and Mohsen Nasseri",
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title = "Predicting failure pressure of the corroded offshore
pipelines using an efficient finite element based
algorithm and machine learning techniques",
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year = "2022",
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journal = "Ocean Engineering",
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volume = "254",
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pages = "111382",
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month = "15 " # jun,
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keywords = "genetic algorithms, genetic programming, Offshore
pipelines, Corrosion, Artificial neural network, ANN,
Genetic programing, Support vector machine, SVM, Random
forest, Gaussian process regression",
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ISSN = "0029-8018",
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DOI = "doi:10.1016/j.oceaneng.2022.111382",
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URL = "https://www.sciencedirect.com/science/article/pii/S0029801822007697",
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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",
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notes = "Also known as \cite{ABYANI2022111382}",
- }
Genetic Programming entries for
Mohsen Abyani
Mohammad Reza Bahaari
Mohamad Zarrin
Mohsen Nasseri
Citations