Modeling the Frequency of Water Main Breaks in Water Distribution Systems: Random-Parameters Negative-Binomial Approach
Publication: Journal of Infrastructure Systems
Volume 23, Issue 2
Abstract
Water main breaks can have significant adverse social, economic, and environmental impacts. As a result, water utilities seek to be proactive and implement asset management programs to reduce the frequency of water main breaks and mitigate their impacts. A key to the success of these asset management programs is the ability to quantify the effect that a variety of factors may have on the likelihood of water main breaks, and hence identify those pipes that need to be inspected frequently. Using water-main break data for a 21-year period from two U.S. cities in the Great Lakes region, this paper demonstrates a methodology to estimate the system-wide monthly frequency of water main breaks as a function of a number of explanatory variables. Using a random-parameters negative-binomial approach, the statistical estimations show that pipe diameters, average pipe age, distribution of pipe age, pipe material, time of year, and mean monthly temperature all have a significant impact on monthly water main break frequencies. The effect that some of these explanatory variables have on break frequencies also varies across months in several cases (as captured by random parameters). Finally, the results clearly show that the relationship between explanatory variables and monthly break frequencies is system specific, as reflected by the many differences in the estimation results between the two water systems considered in this study.
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Acknowledgments
The authors would like to extend their appreciation to the Citizen Energy Group in Indianapolis, IN and City Utilities Engineering in Fort Wayne, IN for their support and collaboration in providing the water distribution characteristics and main breaks data. Citizens Energy Group recognizes the intent of this report and values the general conclusions drawn by its authors. However, Citizens Energy Group does not warrant validity of the source data or the results derived from its use.
References
Anastasopoulos, P., Labi, S., Karlaftis, M., and Mannering, F. (2011). “An exploratory state-level empirical assessment of pavement performance.” J. Infrastruct. Syst., 200–215.
Anastasopoulos, P., and Mannering, F. (2009). “A note on modeling vehicle-accident frequencies with random-parameters count models.” Accid. Anal. Prev., 41(1), 153–159.
Anastasopoulos, P., and Mannering, F. (2015). “An analysis of pavement overlay and replacement performance using random-parameters hazard-based duration models.” J. Infrastruct. Syst., 04014024.
Andreou, S. A., Marks, D. H., and Clark, R. M. (1987). “A new methodology for modelling break failure patterns in deteriorating water distribution systems: Theory.” Adv. Water Resour., 10(1), 2–10.
ASCE. (2013). “Report card.” 〈http://www.infrastructurereportcard.org/〉 (Mar. 2014).
Asnaashari, A., McBean, E., Shahrour, I., and Gharabaghi, B. (2009). “Prediction of water main failure frequencies using multiple and Poisson regression.” Water Sci. Technol. Water Supply, 9(1), 9–19.
Berardi, L., Giustolisi, O., Kapelan, Z., and Savic, D. A. (2008). “Development of pipe deterioration models for water distribution systems using EPR.” J. Hydroinform., 10(2), 113–126.
Bhat, C. (2003). “Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences.” Trans. Res. Part B, 37(9), 837–855.
Bonds, R. W., Barnard, L. M., Horton, A. M., and Oliver, G. L. (2005). “Corrosion and corrosion control research of iron pipe.” J. Am. Water Works Assoc., 97(6), 88–98.
Burn, S., Davis, P., and Schiller, T. (2005). “Long-term performance prediction for PVC pipes.” Subject area: Infrastructure reliability, AWWA Research Foundation, Denver.
Deb, A. K., Hasit, Y. J., Grablutz, F. M., and Herz, R. K. (1998). “Quantifying future rehabilitation and replacement needs of water mains.” AWWA Research Foundation, Denver.
Eisenbeis, P., Rostum, J., and Gat, Y. L. (1999). “Statistical models for assessing the technical state of water networks—Some European experiences.” Proc., American Water Work Association Conf., American Water Work Association, Denver.
Environmental Protection Agency. (2013). “Drinking water infrastructure needs survey and assessment.”, Washington, DC.
Folkman, S. (2012). “Water main break rates in the USA and Canada: A comprehensive study.” Utah State Univ. Buried Structure Laboratory, Logan, Utah.
Gat, Y. L. (2014). “Extending the Yule process to model recurrent pipe failure in water supply networks.” Urban Water J., 11(8), 617–630.
Greene, W. (2007). Limdep, Version 9.0, Econometric Software, Plainview, NY.
Habibian, A., (1994). “Effect of temperature changes on water-main breaks.” J. Trans. Eng., 312–321 .
Herz, R. K. (1996). “Ageing processes and rehabilitation needs of drinking water distribution networks.” J. Water SRT-Aqua, 45(5), 221–231.
Kleiner, Y., and Rajani, B. (2001). “Comprehensive review of structural deterioration of water mains: Statistical models.” Urban Water, 3(3), 131–150.
Kleiner, Y., and Rajani, B. (2002). “Forecasting variations and trends in water-main breaks.” J. Infrastruct. Syst., 122–131.
Kleiner, Y., and Rajani, B. (2010). “I-WARP: Individual water main renewal planner.” J. Drinking Water Eng. Sci., 3, 71–77.
Kleiner, Y., and Rajani, B. B. (1999). “Using limited data to assess future needs.” J. Am. Water Work Assoc., 91(7), 47–62.
Lei, J. (1997). “Statistical approach for describing lifetimes of water mains—Case Trondheim Municipality.”, SINTEF Civil and Environmental Engineering, Trondheim, Norway.
Makar, J., Desnoyers, R., and McDonald, S. (2001). “Failure modes and mechanisms in gray cast iron pipe.” Proc., of Underground Infrastructure Research: Municipal, Industrial and Environmental Applications, Kitchener, ON, 1–10.
Mannering, F., and Bhat, C. (2014). “Analytic methods in accident research: Methodological frontier and future directions.” Anal. Methods Accid. Res., 1, 1–22.
Mannering, F., Shankar, V., and Bhat, C. (2016). “Unobserved heterogeneity and the statistical analysis of highway accident data.” Anal. Methods Accid. Res., 11, 1–16.
Marks, H. D., Andreou, S., Jeffrey, L., and Park, C. (1985). “Predicting urban water distribution maintenance strategies: A case study of New Haven, Connecticut.” U.S. EPA, Edison, NJ.
Milton, J., and Mannering, F. (1998). “The relationship among highway geometrics, traffic related elements and motor vehicle accident frequencies.” Transportation, 25(4), 395–413.
Moser, A. P., and Kellogg, K. G. (1994). “Evaluation of polyvinyl chloride (PVC) pipe performance.” AWWA Research Foundation, Denver.
Najafi, M. (2010). Trenchless technology piping: Installation and inspection, ASCE Press, New York.
Pelletier, G., Mailhot, A., and Villeneuve, J. P. (2003). “Modeling water pipe breaks—Three case studies.” J. Water Resour. Plann. Manage., 115–123.
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.
Rostum, J. (2000). “Statistical modelling of pipe failures in water networks.” Doctoral dissertation, Norwegian Univ. of Science and Technology, Trondheim, Norway.
Seica, M. V., and Packer, J. A. (2004). “Mechanical properties and strength of aged cast iron water pipes.” J. Mater. Civ. Eng., 69–77.
Shamir, U., and Howard, C. D. (1979). “An analytic approach to scheduling pipe replacement.” J. Am. Water Works Assoc., 71(5), 248–258.
Singh, A., and Adachi, S. (2012). “Expectation analysis of the probability of failure for water supply pipes.” J. Pipeline Syst. Eng. Pract., 36–46.
Uni-Bell (Uni-Bell PVC Pipe Association). (2001). Handbook of PVC pipe; design and construction, 4th Ed., Industrial Press, Inc., Norwalk, CT.
U.S. EPA. (2009). “Control and mitigation of drinking water losses in distribution systems.” EPA MC-4606M, EPA 816-D-09-001.
Walski, T. M., and Pelliccia, A. (1982). “Economic analysis of water main breaks.” J. Am. Water Work Assoc., 74(3), 140–147.
Wang, Y., Zayed, T., and Moselhi, O. (2009). “Prediction models for annual break rates of water mains.” J. Perform. Constr. Facil., 47–54 .
Washington, S., Karlaftis, M., and Mannering, F. (2011). Statistical and econometric methods for transportation data analysis, Chapman & Hall/CRC, Boca Raton, FL.
Watson, T. G., Christian, C. D., Mason, A. J., Smith, M. H., and Meyer, R. (2004). “Bayesian-based pipe failure model.” J. Hydroinform., 6(4), 259–264.
Yamijala, S., Guikema, S. D., and Brumbelow, K. (2009). “Statistical models for the analysis of water distribution system pipe break data.” Reliability Eng. Syst. Saf., 94(2), 282–293.
Zamenian, H., Abraham, D. M., and Faust, K. (2015). “Energy loss modeling of water main breaks: A hybrid system dynamics-agent based modeling approach.” Electronic Proc., 5th Int. Construction Specialty Conf., Univ. of British Columbia Library, Vancouver, BC.
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Information
Published In
Journal of Infrastructure Systems
Volume 23 • Issue 2 • June 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 5, 2015
Accepted: Jul 12, 2016
Published online: Sep 8, 2016
Discussion open until: Feb 8, 2017
Published in print: Jun 1, 2017
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