Water Resources Management

, Volume 32, Issue 5, pp 1713–1723 | Cite as

A Superposed Model for the Pipe Failure Assessment of Water Distribution Networks and Uncertainty Analysis: A Case Study

  • Qiang Xu
  • Zhimin Qiang
  • Qiuwen Chen
  • Kuo Liu
  • Nan Cao
Article
  • 51 Downloads

Abstract

Pipe failure often occurs in water distribution networks (WDNs) and results in high levels of water loss and socio-economic damage. Physical-based, statistical and data-driven models have been developed to estimate pipe failure rates (failures per km of pipe per year) to efficiently manage water losses from WDNs and to ensure safe operations. Due to the complexities of pipe failure patterns, we develop a superposed statistical model to depict the relationship between pipe failure rate and pipe age. The model’s level of uncertainty was then quantified by simulating pipe failures as Poisson numbers. Part of Beijing’s WDN is taken as a study case, and pipe failure data for a 4-year period, as well as pipe properties, are collected to develop the pipe failure model. The case study results show that the pipe failure rates vary with time in a non-monotonic manner and that the proposed model captures pipe failure behaviour with an R2 value of 0.95. A 95% confidence interval of modelled pipe failures for each pipe age group is used to describe the uncertainty level of the model. We find that 88% of the observations fall under the 95% confidence interval. The established model could be applied to prioritize pipes with higher failure rates to optimize pipe replacement/rehabilitation strategies. Our uncertainty analysis of this model can help utility managers understand the model’s reliability and formulate reasonable WDN management plans.

Keywords

Pipe failure model Water distribution network Superposed statistical model Uncertainty analysis 

Notes

Acknowledgements

This work was supported by Ministry of Science and Technology of People’s Republic of China (2017ZX07108-002) and by the Jiangsu Science Fund (BE2016617, GHB-HT-2016).

References

  1. Andreou SA, Marks DH, Clark RM (1987) A new methodology for modelling failure patterns in deteriorating water distribution systems: Theory. Adv Water Resour 10(1):2–10CrossRefGoogle Scholar
  2. Araujo LS, Ramos H, Coelho ST (2006) Pressure control for leakage minimisation in water distribution systems management. Water Resour Manag 20(1):133–149CrossRefGoogle Scholar
  3. Berardi L, Kapelan Z, Giustolisi O, Savic D (2008) Development of pipe deterioration models for water distribution systems using EPR. J Hydroinf 10(3):113–126CrossRefGoogle Scholar
  4. Constantine AG, Darroch JN, Miller R (1996) Predicting underground pipe failure. Water (J Austra Water Assoc) 23(2):9–10Google Scholar
  5. Haight FA (1967) Handbook of the Poisson distribution. John Wiley & Sons, New YorkGoogle Scholar
  6. Ho CI, Lin MD, Lo SL (2010) Use of a GIS-based hybrid artificial neural network to prioritize the order of pipe replacement in a water distribution network. Environ Monit Assess 166(1):177–189CrossRefGoogle Scholar
  7. Jafar R, Shahrour I, Juran I (2010) Application of artificial neural networks (ANN) to model the failure of urban water mains. Math Comput Model 51(9–10):1170–1180CrossRefGoogle Scholar
  8. Karadirek IE, Kara S, Yilmaz G, Muhammetoglu A, Muhammetoglu H (2012) Implementation of hydraulic modelling for water-loss reduction through pressure management. Water Resour Manag 26(9):2555–2568CrossRefGoogle Scholar
  9. Kettler AJ, Goulter IC (1985) An analysis of pipe failure age in urban water distribution networks. Can J Civ Eng 12(2):286–293CrossRefGoogle Scholar
  10. Kleiner Y, Rajani B (2001) Comprehensive review of structural deterioration of water mains: statistical models. Urban Water 3(3):131–150CrossRefGoogle Scholar
  11. Le Gat Y, Eisenbeis P (2000) Using maintenance records to forecast failures in water networks. Urban Water 2(3):173–181CrossRefGoogle Scholar
  12. Li ZJ, Chen QW, Xu Q, Blanckaert K (2013) Generalized likelihood uncertainty estimation method in uncertainty analysis of numerical eutrophication models: Take BLOOM as an example. Math Probl Eng.  https://doi.org/10.1155/2013/701923
  13. Lindenschmidt KE, Fleischbein K, Baborowski M (2007) Structural uncertainty in a river water quality modelling system. Ecol Model 204(3):289–300CrossRefGoogle Scholar
  14. Lu YH, Huang ZJ, Zhang T (2013) Method and case study of quantitative uncertainty analysis in building energy consumption inventories. Energ Buildings 57:193–198CrossRefGoogle Scholar
  15. Mailhot A, Pelletier G, Noel JF, Villeneuve JP (2000) Modeling the evolution of the structural state of water pipe networks with brief recorded pipe failure histories: Methodology and application. Water Resour Res 36(10):3053–3062CrossRefGoogle Scholar
  16. Mannina G, Viviani G (2010) An urban drainage stormwater quality model: Model development and uncertainty quantification. J Hydrol 381(3–4):248–265CrossRefGoogle Scholar
  17. Pulcini G (2001) Modeling the failure data of a repairable equipment with bathtub type failure intensity. Reliab Eng Syst Saf 71(2):209–218CrossRefGoogle Scholar
  18. Rajani B, Kleiner Y (2001) Comprehensive review of structural deterioration of water mains: physically based models. Urban Water 3(3):151–164CrossRefGoogle Scholar
  19. Shamir U, Howard C (1979) An analytical approach to scheduling pipe replacement. J AWWA 71(5):248–258Google Scholar
  20. Shen ZY, Hong Q, Yu H, Liu RM (2008) Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China. Sci Total Environ 405(1):195–205CrossRefGoogle Scholar
  21. Singh A, Adachi S (2013) Bathtub curves and pipe prioritization based on failure rate. Built Environ Proj Asset Manage 3(1):105–122CrossRefGoogle Scholar
  22. Watson TG, Christian CD, Mason AJ, Smith MH, Meyer R (2004) Bayesian-based pipe failure model. J Hydroinf 6(4):259–264Google Scholar
  23. Wilkins DJ (2002) The bathtub curve and product failure behavior part one - The bathtub curve, infant mortality and burn-in. Reliab HotWire. No. 21. http://www.weibull.com/hotwire/issue21/hottopics21.htm
  24. Xu Q, Chen QW, Li WF, Ma JF (2011) Pipe failure prediction based on evolutionary data-driven methods with brief recorded data. Reliab Eng Syst Saf 96(8):942–948CrossRefGoogle Scholar
  25. Xu Q, Chen QW, Ma JF, Blanckaert K (2013) Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. J Hydro-environ Res 7:134–140CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Qiang Xu
    • 1
  • Zhimin Qiang
    • 1
  • Qiuwen Chen
    • 2
  • Kuo Liu
    • 3
  • Nan Cao
    • 3
  1. 1.Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina
  2. 2.Nanjing Hydraulics Research InstituteNanjingChina
  3. 3.Beijing Waterworks GroupBeijingChina

Personalised recommendations