Considering Soil Parameters in Prediction of Remaining Service Life of Metallic Pipes: Bayesian Belief Network Model
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 7, Issue 2
Abstract
Water mains are the essential component of a water supply system (WSS) buried underground, and they account for 80% of total system expenditures. Deterioration of this asset, as a result of an aggressive soil environment, is inevitable over a pipe’s service life. Although an increasing number of studies have estimated the remaining service life (RSL) of metallic pipes, most of them either are data intensive or consider limited soil and pipe parameters. In this paper, a Bayesian belief network (BBN) model is proposed to handle the problem of varying data availability and the dependency between parameters. First the proposed approach uses a combination of empirical data, experimental data, expert opinion, and a mathematical model to predict soil corrosivity and pit depth. Then a simple programming logic is used to predict RSL. Finally, the performance of the model is evaluated using a BBN sensitivity analysis. Monte Carlo simulations (MCSs) are performed using randomly generated input from measured statistical parameters (i.e., mean and standard deviation) to indicate the effect of input parameters on metallic pipe RSL and safety index (SI).
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The financial support given to the second and third authors through the Natural Science and Engineering Research Council of Canada (NSERC) Collaborative Research and Development Grant (CRDG) (Number: CRDPJ 434629-12) program is acknowledged.
References
Ahammed, M., and Melchers, R. (1994). “Reliability of underground pipelines subject to corrosion.” J. Transp. Eng., 989–1002.
ASNDT (American Socity for Non Destructive Testing). (2014). “Introduction to non-destructive testing.” 〈www.asnt.org〉 (Jun. 15, 2014).
Bednarski, M., Cholewa, W., and Frid, W. (2004). “Identification of sensitivities in Bayesian networks.” Eng. Appl. Artif. Intell., 17(4), 327–335.
Brander, R. (2004). “Minimizing failures to PVC water mains.” Proc., Plastic Pipe XII Conf., Milan, Italy.
Caleyo, F., Velázquez, J., Valor, A., and Hallen, J. (2009a). “Markov chain modelling of pitting corrosion in underground pipelines.” Corros. Sci., 51(9), 2197–2207.
Caleyo, F., Velázquez, J., Valor, A., and Hallen, J. (2009b). “Probability distribution of pitting corrosion depth and rate in underground pipelines: A Monte Carlo study.” Corros. Sci., 51(9), 1925–1934.
City of Calgary. (2014). “Calgary’s water main repairs and maintenance.” 〈www.calgary.ca〉 (Feb. 20, 2014).
Cole, I., and Marney, D. (2012). “The science of pipe corrosion: A review of the literature on the corrosion of ferrous metals in soils.” Corros. Sci., 56, 5–16.
Costello, S., Chapman, D., Rogers, C., and Metje, N. (2007). “Underground asset location and condition assessment technologies.” Tunnelling Underground Space Technol., 22(5), 524–542.
Coupé, V., and van der Gaag, L. (2002). “Properties of sensitivity analysis of Bayesian belief networks.” Ann. Math. Artif. Intell., 36(4), 323–356.
Demissie, G., Tesfamariam, S., Brander, R., and Sadiq, R. (2015). “Modelling soil corrosivity using Bayesian belief network.” J. Sruct. Saf., in press.
Doyle, G., Seica, M. V., and Grabinsky, M. W. F. (2003). “The role of soil in the external corrosion of cast iron water mains in Toronto, Canada.” Can. Geotech. J., 40(2), 225–236.
Ellis, B., and Wong, W. H. (2008). “Learning causal Bayesian network structures from experimental data.” J. Am. Stat. Assoc., 103(482), 778–789.
Gros, X. (1996). “Non-destructive testing techniques.” Non-destructive testing and data fusion, X. Gros, ed., Butterworth-Heinemann, Oxford, U.K., 43.
Hubbell. (2003). Step 7—Corrosion guide, Shelton, CT.
Krieg, M. L. (2001). “A tutorial on Bayesian belief networks.”, DSTO Surveillance Systems Division, Electronics and Surveillance Research Laboratory, Edinburgh, Scotland.
Landau, D. P., and Binder, K. (2009). A guide to Monte Carlo simulations in statistical physics, Cambridge University Press, Cambridge.
Laskey, K. (1995). “Sensitivity analysis for probability assessments in Bayesian networks.” IEEE Trans. Syst. Man Cybern., 25(6), 901–909.
Li, C., and Mahmoodian, M. (2013). “Risk based service life prediction of underground cast iron pipes subjected to corrosion.” Reliab. Eng. Syst. Saf., 119, 102–108.
Liu, Z., Genest, M., and Krys, D. (2012a). “Processing thermography images for pitting corrosion quantification on small diameter ductile iron pipe.” NDT & E Int., 47, 105–115.
Liu, Z., Kleiner, Y., Rajani, B., Wang, L., and Condit, W. (2012b). “Condition assessment technologies for water transmission and distribution systems.”, U.S. Environmental Protection Agency, Washington, DC.
Marlow, D., Davis, P., Trans, D., Beale, D., and Burn, S. (2009). Remaining asset life: A state of the art review, WERF and IWA Publishing, Alexandria, VA.
Mohebbi, H., Li, C., Freitas, V. P., and Corvacho, H. (2000). “Application of fracture mechanics in reliability analysis of corrosion affected cast iron water mains.” 12th Int. Conf. of Building Materials and Components, Faculty of Engineering of the University of Porto, Porto, Portugal, 1.
Mooney, C. Z. (1997). Monte Carlo simulation, Sage, Thousand Oaks, CA.
Norsys Software. (2014). “Senitivity analysis in Netica Bayesian belief networks.” 〈http://www.norsys.com〉 (Aug. 22, 2014).
Okoh, C., Roy, R., Mehnen, J., and Redding, L. (2014). “Overview of remaining useful life prediction techniques in through-life engineering services.” Procedia CIRP, 16, 158–163.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference, Morgan Kaufmann, San Francisco.
Rajani, B., et al. (2000). “Investigation of grey cast iron water mains to develop a methodology for estimating service life.” AWWA Research Foundation and American Water Works Association, Denver.
Rajani, B., and Makar, J. (2000). “A methodology to estimate remaining service life of grey cast iron water mains.” Can. J. Civ. Eng., 27(6), 1259–1272.
Rajani, B., and Tesfamariam, S. (2004). “Uncoupled axial, flexural, and circumferential pipesoil interaction analyses of partially supported jointed water mains.” Can.Geotech. J., 41(6), 997–1010.
Rajani, B., and Tesfamariam, S. (2007). “Estimating time to failure of cast-iron water mains.” Proc. ICE-Water Manage., 160(2), 83–88.
Randall-Smith, M., Russell, A., and Oliphant, R. (1992). Guidance manual for the structural condition assessment of trunk mains, Water Research Center (WRc), Swindon, U.K.
Romanoff, M. (1957). “Underground corrosion.” National Bureau of Standards Circular, U.S. Government Printing Office, Washington, DC.
Rossum, J. R. (1969). “Prediction of pitting rates in ferrus metals from soil parameters.” J. Am. Water Works Assoc.), 61(6), 305–310.
Sadiq, R., Rajani, B., and Kleiner, Y. (2004). “Probabilistic risk analysis of corrosion associated failures in cast iron water mains.” Reliab. Eng. Syst. Saf., 86(1), 1–10.
SAS v9.1 [Computer software]. SAS Institute, Cary, NC.
Si, X.-S., Wang, W., Hu, C.-H., and Zhou, D.-H. (2011). “Remaining useful life estimation–a review on the statistical data driven approaches.” Eur. J. Oper. Res., 213(1), 1–14.
Spickelmire, B. (2002). “Corrosion consideration for ductile iron pipe.” Mater. Performance, 41(7), 16–23.
Stelzenmüller, V., Lee, J., Garnacho, E., and Rogers, S. I. (2010). “Assessment of a Bayesian belief network-GIS framework as a practical tool to support marine planning.” Mar. Pollut. Bull., 60(10), 1743–1754.
Tesfamariam, S., and Martín-Pérez, B. (2008). “Bayesian belief network to assess carbonation-induced corrosion in reinforced concrete.” J. Mater. Civ. Eng., 707–717.
Tesfamariam, S., Rajani, B., and Sadiq, R. (2006). “Possibilistic approach for consideration of uncertainties to estimate structural capacity of ageing cast iron water mains.” Can. J. Civ. Eng., 33(8), 1050–1064.
Vanier, D. D. (2001). “Why industry needs asset management tools.” J. Comput. Civ. Eng., 35–43.
Velázquez, J. C., Caleyo, F., Valor, A., and Hallen, J. M. (2009). “Predictive model for pitting corrosion in buried oil and gas pipelines.” Corrosion, 65(5), 332–342.
Information & Authors
Information
Published In
Journal of Pipeline Systems Engineering and Practice
Volume 7 • Issue 2 • May 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Sep 25, 2014
Accepted: Oct 6, 2015
Published online: Dec 31, 2015
Published in print: May 1, 2016
Discussion open until: May 31, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.