@article{ref1,
title="Neural-network prediction of damage to accident-prone sections of oil and gas pipelines",
journal="Russian engineering research",
year="2021",
author="Burdina, A.A. and Nekhrest-Bobkova, A.A.",
volume="41",
number="8",
pages="775-778",
abstract="The prediction of possible damage due to accidents on linear sections of oil and gas pipelines is considered. In the proposed mathematical model, the total economic loss includes components associated with damage to the pipeline itself, gas or oil leaks, and environmental pollution. A fully connected neural network is used to calculate the probability of an accident due to equipment wear, while a fully convolutional neural network is used to calculate the probability of an accident due to external mechanical factors. The result is a mechanism for predicting the possible economic loss on the basis of neural networks. © 2021, Allerton Press, published by Springer Nature  Keywords: Pipeline transportation<p /> <p>Language: en</p>",
language="en",
issn="1068-798X",
doi="10.3103/S1068798X21080104",
url="http://dx.doi.org/10.3103/S1068798X21080104"
}