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contributor authorHojjat Gholami
contributor authorShahram Shahrooi
contributor authorMohammad Shishesaz
date accessioned2022-01-30T21:00:29Z
date available2022-01-30T21:00:29Z
date issued11/1/2020 12:00:00 AM
identifier other%28ASCE%29PS.1949-1204.0000478.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4267492
description abstractPredicting the failure pressure of pipelines is of paramount importance in design and integrity management in order for pipes to operate safely, efficiently, and cost-effectively in terms of repair costs. Given the increasing use of pipelines as high-strength materials, an accurate assessment of defective pipelines is of major importance. This study used data mining to investigate the burst pressure of pipelines containing gouge flaws. The required database was collected using nonlinear finite-element analysis. An artificial neural network method was adopted to predict the burst pressure in a gouged pipeline. The methods used in the artificial neural network are the multilayer perceptron (MLP) and support vector regression (SVR) by spline and Gaussian kernels. Finally, these methods were verified by a full-scale burst test, and the results were compared with those of other methods. The results indicated that the SVR Gaussian kernel had an accurate correlation with the results of the full-scale burst test data. However, the MLP results were less accurate than those of the Gaussian kernel. Moreover, the SVR model using the Gaussian kernel, as compared to other previous models, had the highest accuracy in predicting the burst pressure of high-strength pipelines with gouge defects.
publisherASCE
titlePredicting the Burst Pressure of High-Strength Carbon Steel Pipe with Gouge Flaws Using Artificial Neural Network
typeJournal Paper
journal volume11
journal issue4
journal titleJournal of Pipeline Systems Engineering and Practice
identifier doi10.1061/(ASCE)PS.1949-1204.0000478
page9
treeJournal of Pipeline Systems Engineering and Practice:;2020:;Volume ( 011 ):;issue: 004
contenttypeFulltext


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