A numerical external pitting damage prediction method of buried pipelines

Eliceo Sosa, Adrian Verdín Martinez, Jorge L. Alamilla, Antonio Contreras, Luis M. Quej, Hongbo Liu

Research output: Contribution to journalArticlepeer-review

Abstract

The work introduces a numerical external damage prediction method for buried pipelines. The external pitting initiation and corrosion rate of oil or gas pipelines are affected by pipeline age, physicochemical properties of soils and cathodic protection performance as well as coating conditions. Before developing the damage prediction model, the influencing factors were weighed by grey relational analysis, and then the relationship among the pitting depth and the influencing factors of external corrosion was established for corrosion damage prediction through artificial neural network (ANN). Subsequently, the established ANN was applied to predict corrosion damage and corrosion rate for some selected cases, and the neural network prediction model was analyzed and compared to another corrosion rate prediction models. Through the analysis and comparison, a few opinions were proposed on the external corrosion damage prediction and pipeline integrity management.

Original languageEnglish
Pages (from-to)433-444
Number of pages12
JournalCorrosion Reviews
Volume38
Issue number5
DOIs
StatePublished - 1 Oct 2020

Keywords

  • artificial neural network
  • corrosion damage prediction
  • grey relational analysis

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