Modeling of Corrosion Pit Growth in Buried Steel Pipes

AbstractA review of the published literature shows that although intensive research has been conducted on corrosion of steel in soils, accurate prediction of corrosion pit growth remains a serious challenge. This study aimed to develop a new model for predicting the maximum pit depth in buried steel...

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Bibliographic Details
Published in:Journal of materials in civil engineering 2022-01, Vol.34 (1)
Main Authors: Wang, Weigang, Yang, Wei, Shi, Wenhai, Li, Chun-Qing
Format: Article
Language:English
Subjects:
Aeration
Building materials
Buried pipes
Civil engineering
Corrosion
Data mining
Literature reviews
Mathematical models
Modelling
Parameters
Polynomials
Prediction models
Soil properties
Soils
Steel pipes
Technical Papers
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Summary:AbstractA review of the published literature shows that although intensive research has been conducted on corrosion of steel in soils, accurate prediction of corrosion pit growth remains a serious challenge. This study aimed to develop a new model for predicting the maximum pit depth in buried steel pipes and verify it with data obtained from actual corrosion measurements in the field. A method was developed that integrates advanced data mining techniques, shape descriptive modeling, and evolutionary polynomial regression in deriving the underlying relationships between corrosion-influencing factors and model parameters. It was found that the area effect ψ is closely related to the ion content of the soil (Na+,K+, Mg2+, Cl−, and SO42−) and that the correlation between model parameter Ku and soil properties varies among soils with different aeration. It was also found that the developed predictive model exhibits superiority over existing models in quantifying multiple-phase corrosion growth. The proposed method can effectively correlate model parameters with the main contributing factors, which enables researchers and practitioners to accurately predict the maximum corrosion pit depth in buried steel pipes.
ISSN:0899-1561
1943-5533
DOI:10.1061/(ASCE)MT.1943-5533.0004023