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Research Papers: Pipeline Systems

Underground Corrosion Model of Steel Pipelines Using In Situ Parameters of Soil

[+] Author and Article Information
Siti Nor Fariza Mior Mohd Tahir

Faculty of Civil Engineering,
Universiti Teknologi Malaysia,
UTM Skudai, Johor 81310, Malaysia
e-mail: snfariza3@live.utm.my

Nordin Yahaya

Faculty of Civil Engineering,
Universiti Teknologi Malaysia,
UTM Skudai, Johor 81310, Malaysia
e-mail: nordiny@utm.my

Norhazilan Md Noor

Faculty of Civil Engineering,
Universiti Teknologi Malaysia,
UTM Skudai, Johor 81310, Malaysia
e-mail: norhazilan@utm.my

Lim Kar Sing

Faculty of Civil Engineering and Earth Resources,
Universiti Malaysia Pahang,
Lebuhraya Tun Razak, Gambang,
Kuantan, Pahang 26300, Malaysia
e-mail: limkarsing@ump.edu.my

Azlan Abdul Rahman

Faculty of Civil Engineering,
Universiti Teknologi Malaysia,
UTM Skudai, Johor 81310, Malaysia
e-mail: azlan@utm.my

1Corresponding author.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received July 7, 2014; final manuscript received August 23, 2014; published online February 24, 2015. Assoc. Editor: Marina Ruggles-Wrenn.

J. Pressure Vessel Technol 137(5), 051701 (Oct 01, 2015) (6 pages) Paper No: PVT-14-1105; doi: 10.1115/1.4028424 History: Received July 07, 2014; Revised August 23, 2014; Online February 24, 2015

A simple yet practical model to estimate the time dependence of metal loss (ML) in underground pipelines has been developed considering the in situ soil parameters. These parameters are soil resistivity, pH, moisture content, chloride content, and salinity. The time dependence of the ML was modeled as Pmax = ktn, where t is the time exposure, k is ML constant, and n is the corrosion growth pattern. The results of ML and in situ parameters were analyzed using statistical methods such as data screening, linear correlation analysis, principal component analysis, and multiple linear regressions. The best model revealed that k is principally influenced by ressistivity, and n appears to be correlated with chloride content. Model optimization was carried out by introducing several observation criteria, namely, water access, soil color, and soil texture. The addition of these factors has improved the initial accuracy of model to an R2 score of 0.960. As a conclusion, the developed model can provide immediate assessment of corrosion growth experienced by underground structures.

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References

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Figures

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Fig. 1

Summary of boxplot for ML data and soil parameters

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Fig. 2

Extracted component of soil variables for ML constant (k)

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Fig. 3

Extracted component of soil variables for time factor (n)

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Fig. 4

Predicted ML against observed ML

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Fig. 5

Perfect line (x = y) of predicted ML against observed ML

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Fig. 6

New predicted ML against observed ML

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Fig. 7

Perfect line (x = y) new predicted ML against observed ML

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