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Detection and Adjustment of NonstationaryCorrosion Measurements

Richard H. Mccuen*, Dawn E. Klinesmith**

* Professor, Department of Civil and Environmental Engineering, University of Maryland, College Park.

** Research Assistant, Department of Civil and Environmental Engineering, University of Maryland, College Park .

Periodicity:June - August'2013
DOI : https://doi.org/10.26634/jste.2.2.2382

Abstract

Traditionally, power models that are calibrated to compute corrosion loss of steel and other metals are based on time series of measured thickness losses. The models are based on the assumption that the environmental conditions remain constant over the period of data collection. Given changes in climate and air quality laws, an assumption of statistical stationarity for thickness loss measurements is almost certainly not rational. The goal of this study was to provide a framework for detecting nonstationarity in thickness loss data and then for adjusting the measured data found to be temporally nonhomogeneous. The adjusted values would then represent a specific environmental condition over the duration of the data collection. Specific statistical tests are recommended for the detection of nonstationarity. After nonstationarity is confirmed, a procedure to adjust the measured data to a stationary record is presented. The modeling framework is illustrated using two types of data: one-year corrosion loss data of zinc due to variation in atmospheric SO 2 concentrations and steel corrosion loss rates collected over a period of time when SO concentrations varied. The results 2 show that modeling atmospheric corrosion using nonstationary loss data can lead to inaccurate estimates of thickness loss. The adjustment of measured data to a constant environmental state will be beneficial in economic analyses, risk studies, and planning for changes in air pollution laws.

Keywords

Corrosion, Environment, Time Series, Modeling, Statistics, Air Pollution

How to Cite this Article?

McCuen, R.H., and Klinesmith, D.E. (2013). Detection and Adjustment of Nonstationary Corrosion Measurements. i-manager’s Journal on Structural Engineering, 2(2), 9-16. https://doi.org/10.26634/jste.2.2.2382

References

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[2]. Klinesmith, D.E., McCuen, R.H., & Albrecht, P. (2004). “The Effect of Environmental Conditions on Corrosion Rates.” (under Journal review)

[3]. Larrabee, C.P. (1959). “Corrosion Resistance of High-Strength Low Alloy Steels as Influenced by Composition and Environment.” Corrosion. 9(8): 259-271.

[4]. McCuen, R.H. (2003). Modeling Hydrologic Change. CRC Press, Boca Raton, FL.

[5]. Morcillo, M., Almeida, E., Chico, B., & de la Fuente, D. (2002) “Analysis of ISO Standard 9223 (Classification of Corrosivity of Atmospheres) in Light of Information Obtained in the Ibero-American Micat Project, “Outdoor Atmospheric Corrosion, ASTM STP 1421, H.E. Townsend (ed.), Amer. Soc. Testing and Materials Intern., West Conshohocken, PA.

[6]. Shumway, R.H. (1988). Applied Statistical Time Series Analysis. Prentice-Hall, Inc., Upper Saddle River, NJ.

[7]. Tidblad, J., Kucera, V., Mikhailov, A.A., Henriksen, J., Kreislova, K., Yates, T., & Singer, B. (2002a). “Field Exposure Results on Trends in Atmospheric Corrosion and Pollution.” Outdoor Atmospheric Corrosion, ASTM STP 1421, H.E. Townsend (ed.), Amer. Soc. Testing and Materials Intern., West Conshohocken, PA.

[8]. Tidblad, J., Kucera, V., Mikhailov, A.A., & Knotkova, D. (2002b). “Improvement of the ISO Classification System Based on Dose-Response Functions Describing the Corrosivity of Outdoor Atmosphere.” Outdoor Atmospheric Corrosion, ASTM STP 1421, H.E. Townsend (ed.), Amer. Soc. Testing and Materials Intern., West Conshohocken, PA.

[9]. Townsend, H.E. (2001). “Effects of Alloying Elements on the Corrosion of Steel in Industrial Atmospheres,” Corrosion. 57(6): 497-501.

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