Remaining useful life prediction of pipelines considering the crack coupling effect using genetic algorithm-back propagation neural network-Reference-Cited by-同舟云学术

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Remaining useful life prediction of pipelines considering the crack coupling effect using genetic algorithm-back propagation neural network

Published:2024-11 Issue: Volume:204 Page:112330
ISSN:0263-8231
Container-title:Thin-Walled Structures
language:en
Short-container-title:Thin-Walled Structures

Author:

Xie Mingjiang,Wei Ziqi,Zhao Jianli,Wang Yifei,Liang Xihui,Pei Xianjun

Funder

Nanhu Scholars Program for Young Scholars of Xinyang Normal University

National Aerospace Science Foundation of China

Key Program of NSFC-Tongyong Union Foundation

Publisher

Elsevier BV

Reference49 articles.

1. Fracture properties and fatigue life assessment of API X70 pipeline steel under the effect of an environment containing hydrogen;Nguyen;J. Mech. Sci. Technol.,2021

2. Development of an empirical model to predict the burst pressure of corroded elbows of pipelines by finite element modelling;Shuai;Int. J. Press. Vessel. Pip.,2022

3. Recognition of oil & gas pipelines operational states using graph network structural features;Zhang;Eng. Appl. Artif. Intell.,2023

4. Low-cycle fatigue design of welded offshore pipe components: a modern view on ASME B31 code;Xie;Int. J. Fatigue,2022

5. Effect of surface crack dimension on very low cycle fatigue crack growth behavior of circumferential cracked pipes;Song;Thin-Walled Struct.,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predictive Analysis of Crack Growth in Bearings via Neural Networks;Machines;2024-09-01

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