Stochastic corrosion growth modeling for pipelines using mass inspection data
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DOI: 10.1016/j.ress.2018.07.012
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References listed on IDEAS
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Cited by:
- Chengtao Wang & Wei Li & Gaifang Xin & Yuqiao Wang & Shaoyi Xu, 2019. "Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.
- Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
- Zhang, Tieyao & Shuai, Jian & Shuai, Yi & Hua, Luoyi & Xu, Kui & Xie, Dong & Mei, Yuan, 2023. "Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Woloszyk, Krzysztof & Garbatov, Yordan, 2024. "A probabilistic-driven framework for enhanced corrosion estimation of ship structural components," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Yu, Weichao & Huang, Weihe & Wen, Kai & Zhang, Jie & Liu, Hongfei & Wang, Kun & Gong, Jing & Qu, Chunxu, 2021. "Subset simulation-based reliability analysis of the corroding natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
- Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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Keywords
Pipeline; Corrosion growth; Population-based approach; Mass inspection data; Integrity assessment;All these keywords.
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