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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network
Tieyao Zhang, Jian Shuai, Yi Shuai, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 108990-108990
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model
Xingyuan Miao, Hong Zhao, Boxuan Gao, et al.
Reliability Engineering & System Safety (2023) Vol. 238, pp. 109486-109486
Closed Access | Times Cited: 24
Reliability analysis of corroded pipes using MFL signals and Residual Neural Networks
Yinuo Chen, Zhigang Tian, Haotian Wei, et al.
Process Safety and Environmental Protection (2024) Vol. 184, pp. 1131-1142
Closed Access | Times Cited: 12
Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks
Bilal Zerouali, Yacine Sahraoui, Mourad Nahal, et al.
Reliability Engineering & System Safety (2024) Vol. 249, pp. 110236-110236
Closed Access | Times Cited: 12
Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning
Rui Xiao, Tarek Zayed, Mohamed A. Meguid, et al.
Process Safety and Environmental Protection (2024) Vol. 184, pp. 1424-1441
Closed Access | Times Cited: 11
Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction
Umair Sarwar, Ainul Akmar Mokhtar, Masdi Muhammad, et al.
Engineering Applications of Computational Fluid Mechanics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 9
Residual strength prediction of corroded pipelines based on physics-informed machine learning and domain generalization
Tingting Wu, Xingyuan Miao, Fulin Song
npj Materials Degradation (2025) Vol. 9, Iss. 1
Open Access | Times Cited: 1
Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review
Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Hilmi Hussin, et al.
Engineering Failure Analysis (2023) Vol. 155, pp. 107747-107747
Closed Access | Times Cited: 19
Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model
Xingyuan Miao, Hong Zhao
Reliability Engineering & System Safety (2023) Vol. 237, pp. 109369-109369
Closed Access | Times Cited: 18
Three-layer and robust planning models to evaluate the strategies of defense layer, attack layer, and operation layer for optimal protection in natural gas pipeline network
Jun Zhou, Jiaxing Zhu, Guangchuan Liang, et al.
Reliability Engineering & System Safety (2024) Vol. 249, pp. 110196-110196
Closed Access | Times Cited: 6
Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNN Model
Zhuang Qi, Dong Liu, Zhuo Chen
Energy Engineering (2024) Vol. 121, Iss. 3, pp. 821-834
Open Access | Times Cited: 4
Time varying reliability analysis of corroded gas pipelines using copula and importance sampling
Rui Xiao, Tarek Zayed, MohamedA. Meguid, et al.
Ocean Engineering (2024) Vol. 306, pp. 118086-118086
Closed Access | Times Cited: 4
Residual strength hybrid prediction of hydrogen-blended natural gas pipelines based on FEM-FC-BP model
Shulin Li, Yan Yang, Bensheng Huang, et al.
Energy (2025), pp. 135463-135463
Closed Access
Machine learning methods for predicting residual strength in corroded oil and gas steel pipes
Q. Wang, Hongfang Lü
npj Materials Degradation (2025) Vol. 9, Iss. 1
Open Access
A novel assessment method for residual strength of CO2 pipelines with multi defects based on RF-MLP
Yan Li, Zhanfeng Chen, Wen Wang, et al.
Reliability Engineering & System Safety (2025), pp. 111088-111088
Closed Access
3D Fractal Modeling of Non-Uniform Corrosion in Steel Pipes: Failure Behavior Analysis and Structural Integrity Assessment
Pengju Li, Bin Li, Hongyuan Fang, et al.
Reliability Engineering & System Safety (2025), pp. 111111-111111
Closed Access
Prediction of Strain for Dented Pipelines Based on BP Neural Network
Yu Wang, Yuguang Cao, Hailun Zhang, et al.
Mechanics of Solids (2025)
Closed Access
Intelligent framework for reliability evolution of natural gas pipelines subjected to earthquakes
Yihuan Wang, Tian Xu, Shengzhu Zhang, et al.
Thin-Walled Structures (2025), pp. 113414-113414
Closed Access
A DOOBN based approach for dynamic failure assessment of CO2 flooding injection string system
Xinhong Li, Kai Yu, Sihan Li, et al.
Reliability Engineering & System Safety (2025), pp. 111253-111253
Closed Access
Prediction of pipeline fatigue crack propagation under rockfall impact based on multilayer perceptron
Mingjiang Xie, Yifei Wang, Jianli Zhao, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109772-109772
Closed Access | Times Cited: 9
A novel framework for predicting the burst pressure of energy pipelines with Clustered Corrosion Defects
Yi Shuai, Yi Zhang, Jian Shuai, et al.
Thin-Walled Structures (2024) Vol. 205, pp. 112413-112413
Closed Access | Times Cited: 3
Research on Failure Pressure Prediction of Water Supply Pipe Based on GA-BP Neural Network
Qingfu Li, Zeyi Li
Water (2024) Vol. 16, Iss. 18, pp. 2659-2659
Open Access | Times Cited: 3
Intelligent Pressure Monitoring Method of BP Neural Network Optimized by Genetic Algorithm: A Case Study of X Well Area in Yinggehai Basin
Ting Liu, Xiaobin Ye, Leli Cheng, et al.
Processes (2024) Vol. 12, Iss. 11, pp. 2439-2439
Open Access | Times Cited: 3
Assessment of reliability for subterranean corroded pipelines in cold regions using Monte Carlo method and BP neural network
Xiaoli Li, Hemeng Jing, Xiaoyan Liu, et al.
Cold Regions Science and Technology (2023) Vol. 216, pp. 104002-104002
Closed Access | Times Cited: 6
A study of neural network-based evaluation methods for pipelines with multiple corrosive regions
Zhiwei Zhang, LI Song-ling, Huajie Wang, et al.
Reliability Engineering & System Safety (2024), pp. 110507-110507
Closed Access | Times Cited: 2
Reliability-based design optimization of fluid-conveying pipeline structure subjected to in-service loadings
Zhenghong Yao, Hao Jin, Changyou Li, et al.
Reliability Engineering & System Safety (2024) Vol. 256, pp. 110741-110741
Closed Access | Times Cited: 2
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