DOI QR코드

DOI QR Code

Statistical Approach for Corrosion Prediction Under Fuzzy Soil Environment

  • Received : 2012.11.13
  • Accepted : 2013.02.25
  • Published : 2013.03.30

Abstract

Water distribution pipes installed underground have potential risks of pipe failure and burst. After years of use, pipe walls tend to be corroded due to aggressive soil environments where they are located. The present study aims to assess the degree of external corrosion of a distribution pipe network. In situ data obtained through test pit excavation and direct sampling are carefully collated and assessed. A statistical approach is useful to predict severity of pipe corrosion at present and in future. First, criteria functions defined by discriminant function analysis are formulated to judge whether the pipes are seriously corroded. Data utilized in the analyses are those related to soil property, i.e., soil resistivity, pH, water content, and chloride ion. Secondly, corrosion factors that significantly affect pipe wall pitting (vertical) and spread (horizontal) on the pipe surface are identified with a view to quantifying a degree of the pipe corrosion. Finally, a most reliable model represented in the form of a multiple regression equation is developed for this purpose. From these analyses, it can be concluded that our proposed model is effective to predict the severity and rate of pipe corrosion utilizing selected factors that reflect the fuzzy soil environment.

Keywords

Discriminant function;Distribution pipe;External corrosion;Regression analysis;Replacement plan;Soil properties

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Cited by

  1. Environmental Engineering Research in March 2013 pp.2005-968X, 2013, https://doi.org/10.4491/eer.2013.18.1.001
  2. Assessment of the degree of external corrosion of buried water pipelines and source identification of heavy metals due to surrounding soil conditions in humid environment vol.77, pp.12, 2018, https://doi.org/10.1007/s12665-018-7611-3
  3. Comparative analysis for the corrosion susceptibility of copper alloys in sandy soil vol.23, pp.2, 2018, https://doi.org/10.4491/eer.2017.077