Modeling of Corrosion Pit Growth for Buried Pipeline Considering Spatial and Temporal Variability
Publication: Journal of Engineering Mechanics
Volume 147, Issue 10
Abstract
Corrosion of metal in soils has been intensively investigated in the past. However, a review of the published literature shows that there are challenges for researchers to accurately predict the corrosion growth of buried pipes with spatial and temporal variations. This paper intends to develop a methodology to predict the corrosion pit growth for buried pipelines by thoroughly considering the spatial and temporal variability of corrosion processes. The developed method integrates the corrosion science, conditional random field theory, stochastic process, and copula method into an interrelated simulation algorithm for generating corrosion pit growth fields. It is found in the paper that the shape parameter and rate parameter of the gamma process, as well as the correlation structure of corrosion processes, are not only time-variant but also exhibit spatial variability with time. It is also found that the developed method is much superior to other methods in terms of accuracy and effectiveness. The proposed method considers the correlation between corrosion processes in a long pipeline, which is more practical. It can be concluded that generating an accurate corrosion growth field fully requires consideration of the spatial and temporal variability of model parameters.
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Data Availability Statement
All of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Financial support from the Australian Research Council (DP140101547, LP150100413, and DP170102211), and the National Natural Science Foundation of China (Grant No. 51820105014) is gratefully acknowledged.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 21, 2020
Accepted: Mar 12, 2021
Published online: Jul 24, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 24, 2021
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Cited by
- Weigang Wang, Wei Yang, Yadong Bian, Chun-Qing Li, Failure prediction of buried pipeline by network-based geospatial-temporal solution, Tunnelling and Underground Space Technology, 10.1016/j.tust.2022.104739, 130, (104739), (2022).