 | | Mechanics of Solids
A Journal of Russian Academy of Sciences | | Founded
in January 1966
Issued 6 times a year
Print ISSN 0025-6544
Online ISSN 1934-7936 |
Archive of Issues
| Total articles in the database: | | 13148 |
| In Russian (Èçâ. ÐÀÍ. ÌÒÒ): | | 8140
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| In English (Mech. Solids): | | 5008 |
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| Yu Wang, Yuguang Cao, Hailun Zhang, Shiru Li, and Xingfeng Liu, "Prediction of Strain for Dented Pipelines Based on BP Neural Network," Mech. Solids. 60 (1), 164-181 (2025) |
| Year |
2025 |
Volume |
60 |
Number |
1 |
Pages |
164-181 |
| DOI |
10.1134/S0025654424605810 |
| Title |
Prediction of Strain for Dented Pipelines Based on BP Neural Network |
| Author(s) |
Yu Wang (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China;Province Key Laboratory of Safety of Oil DIFFER& Gas Storage and Transportation, China University of Petroleum (East China), Qingdao, 266580 China)
Yuguang Cao (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China;Province Key Laboratory of Safety of Oil DIFFER& Gas Storage and Transportation, China University of Petroleum (East China), Qingdao, 266580 China, ao_yuguang@qq.com)
Hailun Zhang (Pipeline Engineering Department, Qingdao Branch of SINOPEC Petroleum Engineering Corporation, Qingdao, 257000 China)
Shiru Li (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China;Province Key Laboratory of Safety of Oil DIFFER& Gas Storage and Transportation, China University of Petroleum (East China), Qingdao, 266580 China; Province Key Laboratory of Safety of Oil DIFFER& Gas Storage and Transportation, China University of Petroleum (East China), Qingdao, 266580 China)
Xingfeng Liu (College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao, 266580 China; Province Key Laboratory of Safety of Oil DIFFER& Gas Storage and Transportation, China University of Petroleum (East China), Qingdao, 266580 China) |
| Abstract |
Dents are common defects in oil and gas pipelines. Accurately and efficiently predicting the
strain in dented pipelines is crucial for their safety assessment. Currently, there is limited research on
strain prediction models for dented pipelines both domestically and internationally, and related work
on predicting the mechanical behavior of dented pipelines is relatively immature. In view of this, this
study proposes a model for predicting the maximum equivalent plastic strain at dented areas of pipelines using a Back Propagation Neural Network (BPNN). In this study, a static analysis model for
dented pipelines was established using finite element software, and its accuracy was verified through
relevant experiments. Based on the finite element model and combined with the Pearson correlation
coefficient method, the interdependencies between maximum equivalent plastic strain and various key
parameters were analyzed. A comprehensive training dataset was obtained by ranking the parameter
correlations. Using the dataset, a strain prediction model was established through the application of
the backpropagation algorithm and optimization of the number of neurons in the BPNN. The model
was used to predict the maximum equivalent plastic strain at pipeline dents, and the stability of the
model’s predictions for the maximum equivalent plastic strain was validated against experimental data
and random datasets. The results indicate that the prediction errors for experimental data and the random dataset are minimal, demonstrating that the model can accurately predict the strain behavior of
dented pipelines. The predictive model established in this paper can serve as a reference for assessing
pipeline dents in practical engineering. |
| Keywords |
dented pipeline, BP neural network, equivalent plastic strain, prediction equation, safety evaluation |
| Received |
16 October 2024 | Revised |
26 November 2024 | Accepted |
06 January 2025 |
| Link to Fulltext |
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